@article{ 
author = {Karimi, M. and SadeghiNiaraki, A. and HosseininavehAhmadabadian, A.},  
title = {Automatic Recognition of Coded Targets Using Feature Based Matching Algorithms in a Ubiquitous GIS}, 
abstract ={Accurate positioning is an important problem in many fields especially Geographic Information System (GIS). With the advent of ubiquitous computing, a vast and massive change took place in various technologies, and a new generation of GIS, called ubiquitous GIS (UBGIS), was created. One of the most important aspects transformed by ubiquitous computing is in positioning process. ubiquitous GIS and its components provide an environment where position information can be accessed both inside and outside. Computer Vision and Vision based approaches could be a good and appropriate solution to improve positioning accuracy in a Ubiquitous GIS. Using simple and well-known objects such as targets is an appropriate method. Target recognition is important in determining the center and code of the target. In recent years, especial kind of targets called Coded Targets are considered in different fields of vision based approaches and the demand for a Coded Target guaranteeing automatic, error-free correspondence and accurate image point measurement, has been dramatically increased. Therefore, automatically detecting, matching, and determining the center coordinates of Coded Targets are critical issues. Due to various factors in the environment, automatic execution of this process is very difficult and complex or accuracy and speed are not suitable. This paper aims to propose a new method using feature based matching algorithms for automatically recognizing Coded Targets and identifying their centers with sub-pixel accuracy, which can be used to enhance positioning accuracy in ubiquitous GIS. To achieve this aim, feature based matching algorithms and combining local feature detectors and descriptors like SIFT, SURF, and AKAZE are used to find corresponding image points and automatically recognition of Coded Targets. Therefore, suitable matching algorithm is chosen by comparing different matching algorithms. Results show that the best matching algorithm for this usage is SIFT-SURF that means using SIFT descriptors and SURF detectors will lead to best matching results. Then K-means clustering method is applied to distinguish Coded Targets and extract code of target with respect to template targets that are stored in the database. The cluster with the largest number of corresponding points belongs to the template Coded Target. In second stage a bounding box around the matched Coded Target is considered by defining minimum and maximum coordinates of corresponding points around target, so that there is only one target in this boundary. Then the image is cut in this boundary to firstly increase the speed of the calculation, because the search area for an image gets smaller and secondly reduce the possibility of mistake, because the other features and targets in the cut image are almost eliminated. Then center coordinates of Coded Targets are computed by finding contours in this bounding box and fitting a Hough ellipse to central ellipse of target. Finally, the center of this fitted ellipse is computed as the center of Coded Target. The results of implementing the methods are compared with well-known photogrammetry software called Agisoft (modelling and accurate measuring based on basic photogrammetry and computer vision). Results demonstrate sub pixel accuracy {0.574, 0.496 pixel} in center determination in X and Y direction respectively and success possibility of 63% in code recognition.},  
Keywords = {Ubiquitous GIS, Matching, Automatic Target Recognition, SIFT, SURF, K-means},
volume = {7},
Number = {1}, 
pages = {1-13}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-590-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-590-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {KhoshAmooz, G. and Taleai, M.},  
title = {Identification and Ranking Regions of Interest based on Volunteer-Employed Photography and Geo-tagged Photos}, 
abstract ={Identification people's regions of interest and ranking them based on their popularity level have many applications such as tourism development, traffic management and urban planning. Most of interesting places from tourism's view-points are parks, museums, historical places and scenic areas. To identify these places, it is necessary to access people's perception about environment. Accessing to this environmental perception isn't possible from traditional maps or satellite images. One approach to achieve this kind of people's environmental perception is VEP[1]. In this method, people are asked to take photos about subject of research and then the content of these photos are analyzed. So in the VEP method, the content of images is very important part. However, todays with the development of web 2, people share their taken photos from scenic places and interesting subjects. Each photo has metadata about its spatial location, identity and name of its up-loader, title and more. Therefore, researches in the field of geo-tagged photos, analyzed metadata of these photos. However, in this research, we investigated both of photos' content and metadata to rank popular places and it can be said that the integration of VEP and VGI methods is our main contribution. Tehran region 6 was considered as the case study and its geo-tagged photos are extracted from Panoramio site. Then DBSCAN[2] method was applied to extract regions of interest. The DBSCAN method has many advantages that are 1) it is density-base and the place that have dense data points is identified as cluster. Therefore, it is appropriate for our research that density of photos is an indicator of place's popularity 2) Moreover, in this method, there is no need to know the number of clusters previously and 3) the shape of clusters can be arbitrary. Two mandatory parameters of the DBSCAN method are Eps (Neighborhood’s radius) and MinPts (at-least number of points) but there isn't any ideal method to obtain optimal values of these parameters for all applications. Therefore, to find appropriate values, we ran DBSCAN with different parameters and finally we set Eps as 1000 and MinPts as 10, and in result, 17 clusters were identified. The concept of each cluster was identified based on GeoNames POI[3] and unrelated clusters of tourism were removed. Then the popularity score of each region of interest was computed based on its photos' contents, number of photos and number of up-loaders. One of the scene-recognition algorithms was applied to investigate photos' content. The Laleh Park, Saei Park and Ferdosi square achieved high popularity scores. In the next step, popularity of these places in different months, seasons and years were investigated. Totally it can be said that most of the photos were taken in the April and May, or in other words, in the spring. Moreover, the relation between regions of interest and their land-use types were investigated that shows that green-space was significantly more than other land- use types. The detected places were compared with tourist attractions in Tehran region 6 and this comparison showed that natural attractiveness such as parks and gardens have appeared more than other attractiveness in geo-tagged photos. Comparing the computed popularity score of each region of interest, with sum of its scores on GoogleMap and FourSquare, showed that more coincidence exists in the class of very popular places.   [1] Volunteer-Employed Photography [2] Density Based Spatial Clustering With Noise [3] Points of Interest},  
Keywords = {Geo-tagged Photos, DBSCAN, User-Generated Content, Volunteered Geographic Information, Volunteered Employed Photography},
volume = {7},
Number = {1}, 
pages = {15-28}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-466-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-466-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Nowrouzifar, A. and Rashedi, E. and Rajabi, M. A. and Naseri, F},  
title = {Urban Growth Modeling using Integrated Cellular Automata and Gravitational Search Algorithm (Case Study: Shiraz City, Iran)}, 
abstract ={Cities are growing and encountering many changes over time due to population growth and migration. Identification and detection of these changes play important roles in urban management and sustainable development. Urban growth models are divided into two main categories: first cellular models which are further divided into experimental, dynamic, and integrated models and second vector models. In this study, an integrated urban growth model is proposed which is a combination of cellular automata and gravitational search algorithm (GSA). It has been implemented on Shiraz (Iran) to model the urban growth between 1990 and 2000. The proposed integrated model uses GSA to calibrate cellular automata transition rules. The Landsat satellite imageries in 1990 and 2000 with Digital Elevation Model (DEM) of Shiraz are used in this study. Five parameters including distances from major roads, urban neighborhood, slope, distances from attraction centers, and distances from parks and other green spaces are considered to be effective in the urban growth modeling. Based on the results, Kappa coefficient and overall accuracy of the model are 66.54% and 92%, respectively. By using GSA, calibration of cellular automata is facilitated and the proposed integrated model reaches optimal solutions in fewer iterations. The achieved results show that the proposed integrated model can be used for studying urban growth.},  
Keywords = {Urban Growth Modeling, Cellular Automata, Gravitational Search Algorithm, Shiraz City},
volume = {7},
Number = {1}, 
pages = {29-39}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-462-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-462-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Kaffashcharandabi, N. and Alesheikh, A. A.},  
title = {An Improved SVM Based Method for Asthmatic Patient Monitoring in Ubiquitous Health GIS}, 
abstract ={The ever-increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These affecting parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. With the rapid advancement of human knowledge in diverse areas, new science and technology has been offered to aid people in terms of education, food, transportation and health. Ubiquitous computing is one of the newest human developments to enhance individuals’ lives. In recent years, the efficiency of ubiquitous computing in a wide range of applications such as government, health, safety, municipal and transportation have been studied and validated. This paper introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. Our paper proposed a model for prediction of asthma conditions in various scenarios. The asthmatic conditions of patients were predicted accurately by a Graph-Based Support Vector Machine (SVM) which functions anywhere, anytime and with any status. Proposed model is an improved version of the common SVM algorithm with the addition of unlabeled data and graph-based rules in a context space. The study graph was formed by the ::union:: of the training data (L) and the unlabeled data (U). Afterward, the best kernel type for SVM was estimated, and a multi-class SVM algorithm was performed. Initial classification was carried out and the U dataset was tagged. Next the k-nearest neighbor was determined around each training data item followed by the weighing of each edge of the graph (Wij) based on inverse Euclidean distance. This implies that larger weights were given to any unlabeled data close to the training data. Unlabeled data with high weights were assigned the same label as the reference training data and then the position of the data was varied. In this article, “position” means the location of data point in context space. The positional change was performed to closing the unlabeled data to the training data with respect to its weight. Then, the context space was updated and the SVM algorithm was run. This process continued until an acceptable threshold () was reached. At the end of the process, the final labels were assigned to unlabeled data and the PEF conditions of each patient were predicted. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of the city of Tehran, Iran for monitoring 3 different types of asthma. The input data to our asthma monitoring system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results ascertained that 90% of the system’s predictions were correct. The proposed model also improved the estimation accuracy by 12% in comparison to SVM and ACO methods.},  
Keywords = {Ubiquitous GISystem, Asthma, Semi-supervised Prediction, SVM, ACO },
volume = {7},
Number = {1}, 
pages = {41-54}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-540-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-540-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Babaee, S. S. and Khazai, S. and GhasserMobarake, F.},  
title = {Interferometric Processing Time Series COSMO-SkyMed Pictures to Calculate Subsidence Rate of the Ground and Underground Structures}, 
abstract ={The study of surface displacement, particularly subsidence occurred in important ground and underground structures, and identification of surface dynamic behavior of these structures are always one of the major challenges in recent centuries that have too much costs for various organization. So far, there are several tools to measure the surface displacement, among them; one can point to Global Positioning System (GPS) and precision leveling.  These tools measures the surface displacement of the earth at large scales (spatial and temporal) with high precision.  However, despite the advantages of this method, these tools often very costly, time consuming and requires intensive field work, and additional this method carried out over a small area and only on a limited number of target points or on a single line which passes through the area. Therefore, do not extract the full details of the deformation field in space and time. These reasons have caused attention to a method that reduces these limitations. In the resent year one of the newly technique for detected this displacement and subsidence is the Interferometry Synthetic Aperture Radar (InSAR), persistent/permanent scatterer interferometry (PSInSAR) was developed to improve the accuracy and precision of the deformation measurements, even reach to sub-millimeter level. They are several algorithms for deformation monitoring using PSInSAR, this methods uses a huge collection of stable pixels (PSs) for processing which are known as coherent targets how stable the phase over the time. Use of PSs points causes further increasing the spatial density of deformation-tracking points or improve the accuracy of deformation measurements, therefore in this study used of the persistent/permanent scatterer Interferometry Synthetic Aperture Radar (PSInSAR) and height resolution radar image in order to investigation the subsidence of structures and important ground and underground targets in Tehran city between 2014 to 2016. Our radar data set consists of 13 StripMap HIMAGE, right-looking, ascending mode scenes with an image swath of 40 km, which were acquired at 3-m resolution and with HH polarization, between 2014 to 2016, from the SAR sensors on-board the four satellites of the COSMO-SkyMed constellation, at a ~620-km altitude above the Earth’s surface with an LOS incidence angle of 36° at the scene centre, a minimum of 32.4° and a maximum of 35.5° at the near and far range, respectively, In present study, StaMPS persistent scatterer approach, under SBAS and PS algorithms is used for investigation of time series Interferometric analysis. The results of both processing indicated that significant subsidence in southwest part of Tehran city and in 9, 17, 18, 19 and 21 districts of Tehran municipality during this two year, is occurred which average rate of it is around of 61 mm/year. Furthermore, in this study, displacements occurred in the location of some important structures including Milad tower, Azadi stadium, Azadi tower and Mehrabad airport and also surface displacement enroute Tehran subway, based on extracted results from time series radar Interferometric processing and detecting of danger regions, is evaluated. As a result of this evaluation, the regions that are subject of danger (regions exposed to danger), would be identified and introduced to related organizations for suitable prevention function in future.},  
Keywords = {Interferometric Time Series Analysis, Subsidence, COSMO-SkyMed, Permanent Scatterers (PS), Small Baseline Subset (SBAS), Ground, Underground Structures},
volume = {7},
Number = {1}, 
pages = {55-67}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-482-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-482-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Shahidinejad, M. J. and Farnaghi, M.},  
title = {Design and Development of a Web Based Application for Location Based, Semantic Search and Retrieval}, 
abstract ={Search, discovery, and prioritizing important places and facilities is a vital requirement of users in urban environments. Over the past twenty years, web mapping applications have been continuously improving to better proffer location based services to users. A considerable part of users’ search in web mapping application can be addressed by simple distance-based spatial search. However, there are cases where, in order to properly address user requirements, web mapping applications need to consider not only simple spatial criteria but also spatial relationships. Additionally, current web is moving towards machine readable and machine understandable contents through semantic web. Semantic web provides standards and technologies for machines to be capable of understanding documents on the web. Meanwhile, the amount of spatial data that is published on the web is rapidly growing. These data are faced with problems such as diversity, heterogeneity, incompleteness, and therefor are not well prepared for retrieval and utilization. Description of spatial data using semantic web concepts is a solution to most of the mentioned problems. Currently, different standardization organizations in geospatial community are working on development of required standards for semantic spatial data. GeoSPARQL, a semantic query language, is a standard developed to query and retrieve semantically described spatial information over the web. In order to enable citizens to search and recommend urban facilities, this study developed a web mapping application. In this application, GIS and semantic web technologies are integrated to enhance the performance of the system. Search and discovery based on simple geometric relations, topological relationships, spatial operators, semantic relations and descriptive characteristics of features are implemented in the system. The system implements semantic web technologies, ontology, Open World Assumption and spatio-semantic query language. GeoSPARQL ontology is used to define a RDF/OWL vocabulary for representing spatial objects and querying topological and non-topological relations. QallMe ontology is used to define concepts related to the tourism industry including urban facilities. In this system, interests and needs of citizens are considered as well. There are two executive routines in the system, including data entry and query handling. In the data preparation phase, spatial characteristics and attribute information of urban service centers from various sources are gathered and converted to a semantic representation based on the application ontologies. This semantic representation is saved in Parliament triple store. Having the data prepared in the triple store, a user can access the system through a web based user interface and search for required facilities or places within the city. The query is transformed to GeoSPARQL Select queries afterward and executed on the triple store. The responses are presented to the user on a map, developed by Google Maps APIs. In order to demonstrate the feasibility of the developed application, two usage scenarios are thoroughly implemented and described. The implementation results in spatial software comparing the developed application show the general adequacy of the system. Additionally, diversity, heterogeneity and incompleteness problems of spatial information on the web have decreased to a significant extent. The developed system helps citizens to find urban facilities accurately and efficiently.},  
Keywords = {Semantic Web, GeoSPARQL Query Language, Parliament Triple Store, Spatial Topological Relationships, Urban Services},
volume = {7},
Number = {1}, 
pages = {69-84}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-572-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-572-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Aslani, M. and SaadiMesgari, M.},  
title = {Developing Multi-Agent Reinforcement Learning in Adaptive Traffic Signal Control}, 
abstract ={Nowadays, severe traffic congestion in urban areas resulting in different undesirable socio-economic and environmental consequences is inevitable. Infrastructure improvement for preventing these undesirable impacts seems to be necessary. Integration of intelligent transportation systems (ITS) into the existing transportation infrastructure leads efficient operation -using electronic, sensing, information and communication technologies, and advanced control techniques- without building new roads. The main focus of this article is developing multi-agent reinforcement learning for traffic signal control. Two types of agents are employed: (1) Learning traffic signal agents (LTSAs) that interact with the traffic environment in order to find the optimal traffic signal parameters (traffic signal timing) in response to traffic fluctuations. (2) Vehicle agents that are purely reactive. They can detect their forward direction, current driving lane, other vehicles, and the current phase of approaching traffic signal. Also, vehicles can chane their driving lane in order to reach the better driving speed. Unlike vehicles that are reactive and are not able to learn, LTSAs have the ability to learn over time through reinforcement learning. Reinforcement learning originally stems from the study of animal intelligence and has been developed as a major branch of machine learning for solving sequential decision-making problems. It is a useful approach for solving the stochastic optimization problems. It learns the optimal policy of the agent by interacting with the environment in such a way to maximize some numerical value which represents a long-term objective. Reinforcement learning allows traffic signals to automatically determine the ideal behavior for achieving their objectives. In fact, it enables traffic signals to learn and react flexibly to different traffic situations without the need of a predefined model of the environment and also without the need of human intervention. Each time the traffic signal performs an action, it receives a reward signal indicating whether its action has led it closer to realizing their objectives or not. The traffic signal tries to learn a control policy which is a mapping from states to actions that maximizes the expected sum of the received rewards. Two different scenario including single-agent traffic signal control and multi-agent traffic signal control were conducted. In the first scenario, a learning agent controls an isolated intersection by employing two methods of reinforcement learning including Q-learning and State-Action-Reward-State-Action (SARSA). Q-Learning is an off-policy method that updates the value of actions based on the hypothetical actions. In Q-Learning, as long as the traffic signal visits all the state-action pairs, it converges to the optimal action-values. SARSA is an on-policy algorithm that updates action-values on the basis of the experience gained from following some policy. In SARSA, the traffic signal should explore, and stop exploring after a number of steps. The results of the first scenario indicate that Q-Learning outperforms SARSA. In the second scenario, four learning agents control the main street composed of four intersections by employing indirect cooperative Q-Learning. The results of the second scenario reveal that the indirect cooperative Q-Learning controller decreases 81% queue length, 78% travel time, 57% fuel consumption and 73% air pollution when compared to the optimized pre-timed controller.},  
Keywords = {Multi-Agent Systems, Reinforcement Learning, Q-Learning, SARSA, Adaptive Traffic Signal Control},
volume = {7},
Number = {1}, 
pages = {85-100}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-525-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-525-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {GhaffariRazin, M. R. and Voosoghi, B.},  
title = {Efficiency of Multi-layer Artificial Neural Network with PSO Training Algorithm in Ionosphere Time Series Modeling}, 
abstract ={    Ionosphere is a layer in the upper part of atmosphere wide-ranging from 60 km to 2000 km. It has a very significance in radio wave propagation because of, its electromagnetic attributes. Ionosphere is mainly affected by solar zenith angle and solar activity. In the day-time ionization in ionosphere is at the highest level and the ionospheric effects are stronger. In the night-time ionization decreases and the effects of ionosphere gets weaker. One of the most important parameters that define the physical structure of ionosphere is total electron content (TEC). TEC is a line integral of electron density along signal path between satellites to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. The TEC values can be computed from dual frequency global positioning system (GPS), which are the most available observations for studying the earth’s ionosphere. However, because of scatter repartition of dual frequency GPS stations, precise information on TEC over the favorable region is unknown.      Artificial neural networks appeared in the 1980 of the 20th century, it uses physical systems which can be realized to simulate the human brain structure and function of nerve cells. With distributed storage, parallel processing, the ANN has good self-earning, adaptive and associative function, can adapt to the complex and ever-hanging dynamics characteristics. Figure 1 shows the scheme of a three-layer perceptron network. For training of the network and modifications of the weights, there are so many ways. One of the most famous and simplest methods is back-propagation algorithm which trains network in two stages: feed-forward and feed-backward. In feed-forward process, input parameters move to output layer. In this stage, output parameters are compared with known parameters and the errors is identified. The next stage is done feed-backward. In this stage, the errors move from output layer to input layer. Again, the input weights are calculated. These two stages are repeated until the errors reaches a threshold expected for output parameters.      Particle swarm optimization (PSO) is a population based (evolutionary) stochastic optimization technique in which a collection of particles move around in search of space looking for the best solution to an optimization problem. The concept is derived from the motion of a flock of birds that communicates and learns from each other in search for food. This algorithm proposed by Eberhart et al., (2001). A PSO algorithm is inspired on the movements of the best member of the population and at the same time also on their own experience. The metaphor indicates that a set of solutions is moving in a search space with the aim to achieve the best position or solution. In this paper, 3-layer MLP-ANN with 18 neurons in hidden layer is used to modeling the ionosphere TEC time series variations. For this purpose, observations from 36 GPS station in 11 successive days of 2012 (DOY# 220 to 230) are used to processes. To accelerate training step and also enhance the accuracy of the results, particle swarm optimization (PSO) algorithm is used. GPS TEC is used to validate the accuracy of results. Also results of ANN compared with international reference ionosphere (IRI-2012) and universal Kriging method. Analysis of the results showed that the PSO training algorithm has a high-speed in convergence to the optimal solutions. To evaluate the error of ANN results, dVTEC=VTECGPS - VTECM is used. Minimum dVTEC is computed 0.55, 1.57 and 0.70 TECU for ANN, IRI-2012 and universal kriging methods. Also, maximum dVTEC obtained 5.45, 7.16 and 5.51 TECU, respectively. The results of this paper suggest that the artificial neural network with PSO training algorithm has high accuracy in modeling of ionosphere electron content time series. },  
Keywords = {ANN, Back Propagation, PSO, TEC, GPS, IRI2012, Kriging},
volume = {7},
Number = {1}, 
pages = {101-113}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-426-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-426-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {RaoofianNaeeni, M. and Feizi, M.},  
title = {Regional Gravity Field Modelling using Adjausted Spherical Cap Harmonic Analysis}, 
abstract ={In this study, the problem of local gravity field modeling is investigated with thw aid of spherical cap harmonic analysis. To do so, the Dirikhlet boundary value problem for Laplace equation is solved for boundary condition prescribed on the surface of a spherical cap. In this case, the solution of Laplace equation is represented based on linear combinations of associated Legendre function of non-integer degree and integer order. To evaluate the performance of the model, a spherical cap zone located in northwest of Iran with a half-angle of one degree is selected and the local gravity data in the form airborn observations are simulated over the cap region using global geopotential model. Morover the simulated data are contaminated with random noise in order to better adapt with actual airborn observations. Using thses observation in series representation by pherical cap harmonic analysis, then, the geopotential coefficients for local gravity field are computed. Since the govering equations for determination of the cofficients suffer from an ill-condition problem, it is necessary that some regularization schemes are applied. Here, the Tikhonov regularization method is utilized to obtaine the regular solution. To validate the accuracy of proposed model over the cap region, results are compared with observation of gravity at some control points distribute both within the cap and on its margin. },  
Keywords = {Spherical Cap Harmonic Analysis, Local Gravity Field, Airborne Gravity, Geopotential Model},
volume = {7},
Number = {1}, 
pages = {115-124}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-566-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-566-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Nikbayan, M. and Karimi, M.},  
title = {Modeling Urban Vertical and Horizontal Growth using Vector Cellular Automata}, 
abstract ={Rapid urbanization and urban growth are important issues to be considered. Urban development factors recognition can help urban planners and decision makers to understand the consequences of their decisions on urban growth and development .So urban planners, in order to Control the urban growth, use dynamic systems .cellular automata (CA) models have been increasingly used over the last decades to simulate a wide range of spatial phenomena.Urban development is a complex spatio-temporal process that involves both horizontal and vertical growth. Despite growing recognition of the significance of horizontal development, models of urban vertical growth remain limited .This study aims to develop a GIS-based vector cellular automata model for exploring the Horizontal growth and vertical complexities of urban growth according to the height states of existing buildings in the neighborhood and using General Development Control Regulations.The developed model has the capacity to simulate urban growth space and hence vertical growth .series of variables that are used in horizontal model including accessibility, physical suitibility , neighborhood affect and land price.Affactive factors of  urban development are classified in two part: vertical variables and horizontal variables .Slope and Height factors will be used in order to calculate the phizical suitability for undevelop parcel. Euclidean distance and network distance will be used in order to calculate accessibility. neighborhood affect is considered in three component. Also Value of residential, office and retail floor area are computed for property values affect. Vertical model was performed in two different ways. In the first method, undevelop Parcel is assigned a state value according to the height states of existing buildings in the neighborhood in which it is situated. In the second method, using General Development Control Regulations can simulate different height states of building growth. the model has been developed in a very small area of ​​the city of Qom in the years 1385 to 1394 .The results show overall accuracy 60% in horizontal growth and overall accuracy 65 %  in the first method of  vertical growth and overall accuracy 60 %  in the second method of  vertical growth .Therefore, it carries scope of being used to visualize growth for other, similar, cities and help urban planners and decision makers to understand the consequences of their decisions on urban growth and development.},  
Keywords = {: Raster Cellular automata, Vector Cellular Automata,Vertical Growth, Horizontal Growth , Height, QomCity , GIS},
volume = {7},
Number = {1}, 
pages = {125-136}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-427-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-427-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Ghasemian, N. and AkhoondzadehHanzaei, M.},  
title = {Investigating the Performance of the Ensemble Learning Methods using the Feature Selection Method Approach, for the Integration of Reflective and Thermal Classifiers to Identify the Cloud, Cirrus Clouds and Cnow/Ice in MODIS Satellite Images}, 
abstract ={Almost all MODIS images have cloudy regions. Cloud detection and discriminating it from similar objects like snow/ice is a necessary pre-processing step before extracting accurate information. Features for cloud detection can be divided into two categories; textural and spectral features. Using textural features in the visible bands make cloud and snow/ice pixels separable, while cloud and snow can have similar temperature. Thermal features have been used for cloud detection in different heights in MODIS cloud mask (MOD35). Recently variety of studies using ensemble learning methods for land cover classification have been done. In the studies, ensemble learning methods used for classification but in this study, a new application has been introduced; for fusion of classifiers having reflectance and thermal features. Also, in previous studies, the effect of changing feature selection method on the performance of ensemble learning methods has not been examined. So, the purpose of this study is the comparison of the performance of fusion of the reflectance and thermal classifiers using two kinds of ensemble learning methods including boosting and Random Forest (RF) for detection of cloud, cirrus and snow/ice pixels, based on the feature selection method applied. First, some of the Visible-Infrared bands (VIR), 1, 2, 8 and 26 in addition to the thermal bands including 20, 22, 31, 32 and 35 of Terra MODIS were calibrated and the reflectance and Brightness Temperature (BT) values were extracted. Also, three indexes, Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI) and the ratio of bands b1/b2 were computed. Gray Level Co-occurrence Matrix (GLCM) textural features of the above mentioned bands and some of the BT differences including, BT31-BT32, BT33-BT31, BT31-BT20, BT20-BT32, BT22-BT32 were added to the reflectance and thermal input features sets. For selecting the suitable reflectance and thermal features in different kinds of boosting methods including Adaboost.M1, AdaboostSVM, Logitboost and Totalboost, S criteria and Genetic Algorithm (GA) were used, and in RF algorithm in addition to these methods, Recursive Feature Elimination (RFE) and correlation matrix were applied too. After feature selection step, training data were selected manually for cloud, cirrus and snow/ice and fed into the reflectance and thermal classifiers. Classifiers were fused in decision level using the majority vote method. The performance of different cases was compared using producer accuracy, user accuracy and kappa coefficient indexes. For almost all ensemble methods, ignoring which one of the feature selection methods applied, a high cloud and cirrus producer accuracy achieved. RFE and correlation matrix methods in RF algorithm, resulted in 99% and 100% cloud producer accuracy. These values are higher than S criteria and GA. Boosting methods, ignoring the kind of feature selection method, got higher snow/ice producer accuracy than RF algorithm by assigning higher weights to the class that has less training data among classes. Also, boosting methods resulted in higher cirrus user accuracy than RF. Among feature selection methods applied in RF, correlation matrix achieved 91% value for cirrus user accuracy. Finally, agreement to the reference map produced from MOD35 calculated. RF algorithms showed higher agreement with the reference map in comparison to the boosting methods. Highest agreement resulted from RF-RFE with the value of 76% and lowest agreement from Logitboost-GA with the value of 42%. },  
Keywords = {Ensemble Learning Methods, Feature Selection, Cloud, Snow/Ice, Cirrus, Fusion, Refelective and Thermal Classifiers},
volume = {7},
Number = {1}, 
pages = {137-155}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-550-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-550-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Javadnia, Eslam and Abkar, A. A.},  
title = {Effect of Dust Storm on Optical and Radiative Properties of Aerosols Over Middle East}, 
abstract ={The Middle East is one of the hot-spot regions that has reported the highest occurrence of dust storms. In this study changes in aerosol optical and radiative properties during dust storm over the Middle East on July 2009, was analyzed using retrieved Aerosol Optical Depth (AOD) from Simplified Aerosol Retrieval Algorithm (SARA) algorithm over study regions and ground-based observations from CIMEL Sunphotometer measurements at AERONET (AEronet RObotic NETwork AERONET) site at Kuwait University. Dust plumes captured well with the SARA AOD during dusty days. Maximum AOD values occurred on the 4 July in Kuwait, Ahvaz, Iran, the Persian Gulf, Baghdad and Riyadh where values of 1.44, 2.56, 1.07, 3.0, and 1.99 were recorded, respectively. The maximum aerosol volume size distributions (VSDs) at AERONET site occurred on dusty days and minimum VSDs on non-dusty days. The single scattering albedo (SSA) obtained higher values on dusty days compared to non-dusty days, for all wavelengths (440, 675, 870, and 1020 nm) at the AERONET site. The maximum SSA value of 0.99 occurred at a wavelength of 1020 nm, which is an indication of the dust aerosol. The asymmetry parameter (AP) obtained higher values at shorter wavelengths over the study period. The AP (AP-T) for both modes (fine and coarse) values were higher in the near infrared region than in the visible spectral region on both dusty and non-dusty days. The coarse AP (AP-C) values were higher in the visible spectral region than in the near infrared region on dusty days with the reverse being true on non-dusty days. The VSD, SSA, AP, and refractive index values on dusty days suggested that dust aerosols were predominant over anthropogenic aerosols in the study area. Effect of aerosol on Downward Surface Shortwave Radiation (ARFDSSR) on both dusty and non-dusty days ranged between -51 and -160 Wm−2 (average: -90 Wm−2) at the earth's surface. Effect of aerosol on Net Surface Shortwave Radiation (ARFNSSR) on both dusty and non-dusty days ranged between -39 and -122 Wm−2 (average: -69 Wm−2) at the earth's surface. Dust-induced turbid conditions caused significant extinction of 15–20%, in DSSR, resulting in an increase of 66% in aerosol radiative forcing (ARF) at the AERONET site at Kuwait University. The HYSPLIT back-trajectory analysis revealed that the air masses reached Kuwait from the western part of the Sahara Desert in northwest Africa and Saudi Arabia over the Middle East. Aerosol vertical profiles retrieved by the space-borne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) reveal a well-mixed dust layer occurred over the study area. The aerosol types identified by CALIPSO in the near of the study regions include both dust and polluted dust, but dust aerosols were dominant over the anthropogenic (polluted) aerosols. The CALIPSO aerosol profile indicated a layer of thick dust extending from the surface to an altitude of about 6 km. The results of radiative forcing of dust storm in the Middle East show that mineral dust cause decrease of shortwave radiation and net radiation at the surface. Additionally the presence of mineral dust cause decrease of temperature at earth’s surface. So, dust directly influence the earth’s radiative budget and cause surface cooling.},  
Keywords = {Dust Storm, AOD, MODIS, AERONET, ARF},
volume = {7},
Number = {1}, 
pages = {157-173}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-610-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-610-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Khazaei, S. and Mosavi, V.},  
title = {Detecting and Tracking Moving Objects in Unmanned Aerial Vehicles (UAVs) Images}, 
abstract ={Unmanned Aerial Vehicles (UAVs) are promising tools for many applications, including agriculture, mining, recreation, search and rescue, infrastructure monitoring, and wildlife research and conservation. Many of these applications require some type of object tracking. In fact, one of the most valuable capabilities of UAVs is their ability to detect and track particular moving targets on the ground. The main problem in moving target tracking using UAVs is the background movement with the intended target. In this study, a new method for detecting and tracking moving targets using frame-to-frame registration is presented. The proposed method does not require other external sources to know about the position and orientation of the platform. Also, computer vision processes are applied to detect moving objects and the detections are passed to a tracking algorithm, which then generates continuous tracks of objects seen by the camera. This paper first reviews the current methods utilized in multiple target tracking in video with particular emphasis on airborne applications. It then presents a method for detecting moving objects in video obtained from a UAV. In this method, the improved Speeded-Up Robust Features (SURF) matching algorithm that adds color information to conventional SURF descriptor is used to enhance matching results. The SURF algorithm is widely applied in the field of target detection and tracking. But in this study, the advantages of SURF and color information are combined to achieve better results. Then, Random Sample Consensus (RANSAC) technique is used to remove weak matches. After finding reliable corresponding points in consecutive frame images, the projective transformation between two frames is calculated. Finally for detection the moving object, frame subtraction and image segmentation methods are used respectively. Generally, the proposed method can be summarized like this: the suggested matching method using the improved SURF matching algorithm. The proposed method is implemented on video data obtained from a UAV which has a HD camera with the frame rate of 60 frames per second (fps) and the resolution of 1080×1920 pixels. It is studied to detect moving vehicles and people and the derived outcomes are analyzed. Experimental results show that the proposed method has suitable performance for moving object detection in images with moving background. However, the results show that although the improved SURF matching algorithm imposes some computational complexities to the proposed method, it eventuates to positive effects in matching results. Average processing time of the proposed method is also studied. Results show that although the average processing time is increased about 6%, the matching accuracy increased by an average of 8.5%. However, due to the important role of the matching accuracy on the accuracy of final results obtained from proposed algorithm, using the improved SURF matching algorithm is an appropriate choice. Also, other deliberations show efficiency of the proposed method in detection and tracking moving objects. It is also suggested that in segmentation stage, the performance of other techniques like Fuzzy segmentation algorithm would be evaluated in comparison with Niblack technique. Also the gained results of the proposed algorithm would be evaluated on the data recorded in different environmental conditions like different lightening situation, excessive shaking of the camera and foggy and dusty weather.},  
Keywords = {Detecting, Tracking, Moving Objects, Unmanned Aerial Vehicles, SURF},
volume = {7},
Number = {1}, 
pages = {175-184}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-491-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-491-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Farhanj, F. and Akhoondzadeh, M.},  
title = {Spatial Resolution Enhancement of Thermal Bands in Multi-Spectral Images Using Contourlet Method}, 
abstract ={Thermal infrared bands contain important information for various applications. Their spatial resolution is relatively low, and it is hard to determining the location of the targets in these bands. The aim of this study is to enhance spatial resolution of thermal bands. Image fusion is one of the efficient methods that are employed to enhance spatial resolution of thermal bands by fusing these data with high spatial resolution visible bands. Image fusion aims to integrate images with different spatial and spectral resolution, such that the synthesized image is more suitable for human visual perception or further processing, such as image classification, segmentation, texture feature extraction, object recognition, etc. We chose pixel level image fusion and added details and spatial information taken from the visible band to the thermal infrared band. Multi-resolution analysis (e.g. wavelet, laplacian pyramid, contourlet, curvelet, etc) is an effective pixel level fusion approach. In this paper, the contourlet transform in image fusion due to its advantages, high directionality and anisotropy is used. Because of the downsampling and upsampling, the contourlet transform lacks of shift invariance and results in artifacts. Therefore, we use the other kinds of contourlet transform, nonsubsampled and sharp frequency localization contourlet transform, and then, the image fusion performance of six multi-resolution transforms,  including the discrete wavelet, stationary wavelet, laplacian pyramid, contourlet, nonsubsampled and sharp frequency localization contourlet transform are compared. The methods have been tested using thermal infrared and visible landsat-8 data. The spectral and spatial quality assessment parameters (e.g. CC, SAM, ERGAS, SNR, RMSE, AG, UIQI, etc) show that the sharp frequency localization and the nonsubsampled contourlet transforms perform better than the discrete wavelet and the original contourlet transforms in terms of preserving radiance spectral information and increasing spatial details The experimental results show that the discrete wavelet and the contourlet transform are the worse than the other transformations. Therefore, the shift invariant property is of great importance for image fusion. The results of our comparative analysis show that in spite of the lack of a spectral overlap between the visible and the thermal infrared bands, the final fused thermal image keep its spectral characteristics while the spatial resolution is enhanced. It can be concluded that the sharp frequency localization contourlet transform with redundancy factor of 2.33 is the best technique for fusion of the visible and the thermal infrared bands. Finally, the effect of decomposition levels, on fusion performance result by sharp frequency localization contourlet transform with redundancy factor of 2.33 was investigated. It also can be concluded the appropriate setting for the number of decomposition levels is four.},  
Keywords = {Fusion Bands, Multi-resolution Analysis, Contourlet, Spectral Quality Assessment, Spatial Quality Assessment},
volume = {7},
Number = {1}, 
pages = {185-202}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-485-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-485-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Moradi, M. and Sahebi, M. R.},  
title = {Feature-Based Change Detection of Urban Areas using Particle Swarm Optimization and Genetic Algorithm}, 
abstract ={Nowadays spatial data and urban areas rapidly changing due to the many kinds of natural and artificial factors. These changes lead to the loss of reliability of the information for urban planning, resource management and inefficiency of spatial information systems. so, monitoring of these changes and obtaining update information about the land use and the kind of its changes is essential for urban planning, proper resource management, damage determination assessment and the updating of geospatial information systems. Therefore, more accurate change detection is a challenge for experts and researchers of remote sensing and photogrammetry. In recent years, various techniques have been developed for change detection especially on high-resolution images that choosing the appropriate method and algorithm to identify changes is not easy. Despite all the efforts of researchers to develop different methods for change detection, all techniques and methods have advantages and limitations. This article introduces a new category of changes detection methods. In general, methods and techniques of change detection in urban areas can be categorized into four major categories: direct comparison- post classification dipole, object based- pixel based dipole, supervised- unsupervised dipole, textural and spatial information and features. Despite a rich and useful spectral information in high-resolution satellite images of remote sensing and photogrammetry, just use of this kind of information, will not be enough to achieve the required accuracy due to increased variability within homogenous land-cover classes. So, in this paper, in addition to the spectral features, it is also used texture features extracted from the spatial and frequency domain (Spectral, Anomaly, Edge, Morphological building index (MBI), Other color space, Gray Level Co-occurrence Matrix (GLCM), Features extracted from wavelet transform, Features extracted from Gabor filter, Features extracted from Fourier transform and Features extracted from curvelet transform) to solving this problem and generating changes mask of high-resolution images. The diversity and variety of extracted features from the spatial and frequency domain require optimization algorithms to achieve optimum features. Therefore, particle swarm optimization and genetic algorithms have been used to achieve optimum features and optimum parameters of support vector machine simultaneously. Also according to the major weakness of post classification method for detection of intra-class changes and bad radiometric conditions of used images for segmentation, 2-class classification of differential features is used to detect changes. QuickBird (0.6 m -  October 2006) and GeoEye (0.5 m - August 2010) satellite imagery of AzadShahr/Tehran/Iran are used to evaluate the proposed method. The overall accuracy 93.45 and kappa coefficient 0.87 versus 91.03 and 0.82 show that particle swarm optimization is better than a genetic algorithm to achieve optimum features and optimum parameters of support vector machine simultaneously. It also calculates the effectiveness of each 10 kinds of features used by three criteria introduced in this paper (Effectiveness, Minor Effectiveness, and Overall Effectiveness), indicates the efficiency of using other color spaces, features extracted from wavelet and features extracted from spatial domain (Gray Level Co-occurrence Matrix) and also reflects the weakness of using only spectral data to detect changes in high-resolution images. Compare the proposed approach with other studies (post classification and fuzzy thresholding method) show the effectiveness of proposed method. },  
Keywords = {Change Detection, Texture, Particle Swarm Optimization, Genetic Optimization, Effectiveness Criteria},
volume = {7},
Number = {1}, 
pages = {203-222}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-530-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-530-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Niazmardi, S. and Safari, A. and Homayouni, S.},  
title = {A Novel Method Based on the Multiple Kernel Learning Algorithms for Crop Mapping using Multivariate Satellite Image Time-Series}, 
abstract ={Satellite image time-series (SITS) data are a set of satellite images acquired from the same geographic area over a period of time. SITS data, due to their ability to capture the dynamic spectral behavior of the crop during their growing season, have been increasingly used for accurate crop mapping. The time-series obtained from the multispectral or hyperspectral sensors can be considered as multivariate time-series. The classification of these type of time-series data is a challenging task. This is mainly due to the fact that these data can be considered as a four-dimensional data, so the available classification algorithms cannot be used for their classification. To address this issue, in this paper by using the Multiple Kernel Learning (MKL) algorithms, a novel method for classification of multivariate time-series data is proposed. MKL algorithms are a group of kernel learning algorithms that make it possible to use a combination of kernels instead of a single one for kernel-based learning algorithm such as classification.  In the proposed method, initially one kernel is constructed from data of each time of the time-series and then by using the MKL algorithms, these kernels are optimally combined into a composite kernel. The obtained composite kernel, once constructed, can be used to classify the time-series data by using all the kernel-based classification algorithms.  In order to evaluate the proposed method, two time-series data were used. Both these time-series consisted of 10 different RapidEye imageries, acquired over an agricultural area in Manitoba, Canada. Both these time-series contained the main crop types of the region such as wheat, corn, canola, and soybean. In order to evaluate the effects of different MKL algorithms in the framework of the proposed method, in addition to the common MKL algorithms, the Generalized Multiple Kernel Learning algorithm (GMKL) was adopted as the MKL algorithms in the proposed Method. The GMKL is one of the most recent MKL algorithms proposed in machine learning literature, which has not been evaluated for the time-series data analyses. As a benchmark for comparison with a single kernel method, stacking method, in which the data acquired at different times are stacked into a single data cube, was used. The composite kernel obtained from the proposed algorithm with adopting different MKL algorithms and the kernel constructed from the data cube obtained from the stacking of the data were used to train of a Support vector machine algorithm. The obtained classification accuracies of the SVM showed a dramatic increase (at least 4.9% increase in the overall accuracy of the classification) in the case of using the kernel obtained from the proposed method in comparison with the case of using the kernel obtained from the stacked data cube. Moreover, the GMKL algorithm showed a higher performance in comparison to other MKL algorithms in the context of the proposed method for multivariate time-series classification.  In addition, the proposed method showed better performance in the presence of cloud and cloud shadows in the data. This is because the MKL algorithms can reduce the negative effects of the cloud contaminated images within the time-series.},  
Keywords = {Time-Series Classification, Multivariate Time-Series, Multiple Kernel Learning, Generalized Multiple Kernel Learning},
volume = {7},
Number = {1}, 
pages = {223-233}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-576-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-576-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {AminiAmirkolaee, H. and Arefi, H.},  
title = {3D Semantic Labeling using Region Growing Segmentation Based on Structural and Geometric Attributes}, 
abstract ={Nowadays, automatic point cloud processing is an important and challenging topic in photogrammetry and remote sensing. The LiDAR has the ability of collecting the accurate 3D point cloud from the earth surface, directly. Moreover, recent advances in image processing provide the capability of producing 3D point clouds with high accuracy using dense matching from the digital aerial images. The point cloud segmentation and classification algorithms are usually time consuming and have high computation cost. In this paper a difference object-based approach was proposed for point cloud classification. In this approach, at first the points were segmented into some regions; then these regions were classified into considered classes. In this regard, firstly some boxes with predefined side size were placed on point clouds and each box was analyzed separately. In order to reduce the point density, the points in each box were removed except the nearest point to the center. Then, the region growing algorithm was employed to segment the points with reduced density based on normal vector and curvature value of each point. Afterward, around of each segmented point was searched for labeling the remains points. In other words, the points which have normal vector close to considered point were labeled same as that point. After point segmentation, for each segment some potentially features were selected and produced in order to detect buildings, vegetation as well as grounds. The features should be selected accordance with the geometrical and structural characteristics of the objects. In this paper some features including mean curvature, area, perimeter, boundary irregularity, flatness, elevation, and being terrain or off- terrain were generated. The Alpha shape is a triangulation based algorithm which has the ability of reconstructing the object shape using a set of dense and irregular points. The Alpha value determines the level of details in the reconstructed shape. After computing the shape of the considered segment using Alpha shape algorithm, calculating the area and perimeter was feasible. In order to analyze the boundary irregularity of the segments, the ratio of area between two reconstructed Alpha shapes with two different Alpha values is computed. For each segment a plane was approximated using the MSAC algorithm and the ratio of points in that plane and out of that plane was computed as flatness value. The SMRF algorithm was employed for specifying the off- terrain points. The height of an off-terrain point was acquired by computing the difference between that point and the closest terrain point. Thus, for each segment a feature vector was obtained. Finally, some training data was collected and the segments were classified by KNN algorithm. The proposed approach was implemented and evaluated in 6 different test areas. Although the area 1, 2, 5 and 6 were acquired by LiDAR, the point density of area 1 and 2 is equal to 4 point per m2 and the point density of area 5 and 6 is equal to 65 points per m2. The area 3 and 4 were acquired by dense matching of digital aerial images and theirs average point density is equal to 20 points per m2. The accuracy of proposed approach in area 1 to 6 were 92.25%, 93.44%, 91.44%, 89.23% 92.46% and 89.73%, respectively. The evaluation results clarify the good performance of proposed approach in different areas with various land covers and point densities.},  
Keywords = {Point Cloud, Segmentation, Features, Classification},
volume = {7},
Number = {2}, 
pages = {1-16}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-578-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-578-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Amraei, E. and Mobasheri, M. R.},  
title = {Introducing a Method for Detection of the Suspected Bit_Flip Noise Affected Pixels in Landsat Images}, 
abstract ={Presence of different noises in Landsat images renders the extraction of descent information hard or sometimes impossible. One of these noises is Bit-Flip noise that happens when the signals are transferring from satellite to the other platforms and/or ground stations. During this transferring process, one bit might be affected and altered (e.g. 1 to 0 or 0 to 1). Clearly if the position of this bit is in higher bit values, then the change might be as large as the signal itself. This for dark pixels might be even few orders of magnitudes larger. In this work, a novel method for detection of affected bit and its correction is introduced. In this method, first we used a fuzzy detector method to identify the noise affected pixels. Then the pixels DN values were compared with the neighboring pixels and as results the affected pixels and the degree of affection were calculated. Since the change must be of the order of 2n, the proper value will be identified and corrected accordingly. The method was compared with the other well-known methods using statistical parameters such as SSIM and PSNR. The value for SSIM and PSNR were 0.9 and 28db respectively which were much better compared to other methods. },  
Keywords = {Bit-flip Noise, Satellite Images, Landsat, Remote Sensing},
volume = {7},
Number = {2}, 
pages = {17-26}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-467-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-467-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Ghanbari, H. and Homayouni, S. and Safari, A. and Mohammadpour, A.},  
title = {Hyperspectral Images Classification using Gaussian Mixture Model and Gibbs Sampler Algorithm}, 
abstract ={Hyperspectral image contains hundreds of narrow and contiguous spectral bands. Because of this high spectral resolution, hyperspectral images provide valuable information from the earth surface materials and objects. By advances in remote sensing technology and production of hyper spectral data with high spatial and spectral information, using such data for a detailed study of the phenomenon is spreading quickly. One of the most important applications of hyperspectral data analysis is either supervised or unsupervised classification for land cover mapping. Among different unsupervised methods, Gaussian mixture model has attracted a lot of attention, due to its performance and efficient computational time. Gaussian Mixture Models (GMMs) have been frequently applied in hyperspectral image classification tasks. The problem of estimating the parameters in a Gaussian mixture model has been studied in the literature. Gibbs sampler is one of the methods that can be applied for this problem. Another method for estimation the parameters of a Gaussian mixture model is Expectation-Maximization (EM) algorithm. EM is a general method for optimizing likelihood functions and is useful in situations where data might be missing or simpler optimization methods fail .On the other hand, the large number of bands in a hyperspectral images leads into estimation of a large number of parameters. In the other point of view, the enormous amount of information provided by hyperspectral images increases the computational burden as well as the correlation among spectral bands. Thus, dimensionality reduction is often conducted as one of the most important steps before target detection to both maximize the detection performance and minimize the computational burden. In this paper, we use PCA and Random Projection (RP) for solving the high dimensionality of the data. In order to evaluate the proposed algorithm in real analysis scenarios, we used two benchmark hyperspectral data sets collected by AVIRIS and Reflective Optics System Spectrographic Imaging System (ROSIS). In order to evaluate the effectiveness of the proposed method which is based on the using GMMS and its parameter are estimated using Gibbs sampler method we used two well-known dataset ROSIS and AVIRIS hyperspectral images which they are acquired from a urban and agricultural area, respectively. Moreover, for better evaluation we used a simulated data which is attained using a toolbox which is known as HYDRA project. Investigations on the simulated dataset and two real hyperspectral data showed that the case in which the number of bands has been reduced in the pre-processing stage using either RP or PCA in the feature space, can result the highest accuracy and efficiency for thematic mapping. We also demonstrated that the superiority of the Gibbs sampler in comparison with EM algorithm for estimating the GMM parameters. For instance, in Pavia university dataset, the overall accuracy and Kappa coefficient was 88.80 and 0.84, respectively for GMM-Gibbs-RP method and for GMM-EM-RP method the overall accuracy and kappa coefficient was 84.21 and 0.80, respectively. In other view point, in urban area (Pavia university dataset) with small structures, the amount of improvement in by Gibbs sampler in comparison with EM algorithm was more than the AVIRIS dataset which is related to agricultural area with bigger regions. This shows the capability of Gibbs sampler in confronting with singularities.},  
Keywords = {Classification, Gaussian Mixture Model, Gibbs Sampler, Dimension Reduction, Hyperspectral Image},
volume = {7},
Number = {2}, 
pages = {27-38}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-474-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-474-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Delavar, M. R. and Bahrami, M. and Zare, M.},  
title = {Uncertainty Management using Interval Mathematics and Granular Computing in Seismic Vulnerability Assessment}, 
abstract ={Earthquake as the most devastating natural disaster in urban areas causes huge physical and human damages worldwide. One way to assist reducing the impact of the earthquake on people and infrastructures is to produce a reliable seismic vulnerability map. The physical seismic vulnerability of a region as a multi criteria problem is concerned with seismic intensity, land slope, the number of building floors, building age and quality. Among the most important sources of uncertainty in determining the vulnerability of each urban statistical unit, is the uncertainty related to the conflicts in expert opinions concerning the level of severity of the seismic vulnerability. The main objective of this paper is to manage uncertainty considering different vulnerability classes allocated by the experts in integration of the concerned parameters. In this model, to reduce the uncertainty in the decision making process related to the expert opinions on allocating a seismic physical vulnerability class to each urban statistical unit, interval mathematics, genetic algorithm and granular computing methods are used. The physical seismic vulnerability map has been produced for Tehran on the basis of activation of North Tehran fault. Among 3174 urban statistical units, 150 randomly selected samples have been selected by 5 experts in related geoscience fields. The experts are asked to fill a questionnaire for allocating the physical seismic vulnerability of the samples. Due to the disaggregation in the experts’ knowledge on the physical seismic vulnerability of each statistical unit, their opinions have been integrated using interval mathematics. For the conflict resolution among the experts, genetic algorithm is used. Granular computing has been applied to manage the uncertainty caused by the large amount of information achieved from the parameters affecting the physical vulnerability to assess the seismic physical vulnerability. The relations among the input parameters and the vulnerability classes are presented in a decision table. The rules with a minimum conflict from the decision table are extracted. The vulnerability classes have been sorted from 1 to 5 considering 1 as the least vulnerable class and 5 as the most vulnerable class. According to the results, most of the statistical units in Tehran fall within interval class vulnerabilities of [3 4] and [5 4]. To compare the similarity between the results of the model and those of the previous research by Khamespnah in the same study area, who used an integrated model of granular computing and rough set theory, Spearman rank correlation coefficient was employed. The value of this coefficient was 0.47 that shows some similarities between the results. The accuracy of 76% was achieved in this research using Kappa index verifying the importance of managing uncertainty using interval mathematics.},  
Keywords = {Uncertainty, Interval Mathematics, Granular Computing, Genetic Algorithm, Physical Seismic Vulnerability Assessment},
volume = {7},
Number = {2}, 
pages = {39-52}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-621-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-621-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Khazaei, S. and Karami, A.},  
title = {An Automated Method for Visual Camouflage of Targets with their Background using Greedy Algorithm}, 
abstract ={Camouflage is the art of disguising or blending objects into a natural background so as to make them more difficult for viewers to see. Traditional camouflage is usually based on the designer's experience and includes macro patterns spots and stripes irregular whose outlines or boundaries are sharp and are easier to see. To overcome this main drawback, digital camouflage combines macro and micro patterns with computer assistance. Most works related to the digital camouflage are in the field of color pattern design. However, designing a suitable color pattern that can best match the target in terms of shape and color characteristics with the background is a major challenge in the field of digital camouflage. Nowadays, the digital camouflage is based on the principles of visual psychology and uses digital image processing techniques to characterize background features. The common digital camouflage techniques are based on the fuzzy, the neural network and the greedy methods. The main problem to use these methods is that the number of main colors is chosen manually or experimentally, while it is different in each image. Therefore, the optimal colors cannot be obtained for appropriate blending targets into their backgrounds.   The main objective of this study is to provide a novel method of designing a digital template which automatically extracts the number of original colors based on the specific features of each image.  The proposed method is based on the conventional greedy algorithm. The greedy algorithm tries to minimize the difference between the shape perceived by the viewer and the shape patterned on the target. The proposed method first uses the minimum description length (MDL) criterion for determining the number of optimum clusters of the image. Then, it uses the well-known K-means clustering method to extract the original colors from the image. Finally, the proposed method uses the greedy algorithm to obtain an optimal distribution or arrangement of the combination of pattern templates stored in a database. In this study, the proposed method is compared to the color similarity algorithm proposed by yang and Yin (2015). The quantitative and qualitative assessments of both the methods are based on the saliency map, which is a common criterion for the camouflage assessment. The saliency map is originally intended to model covert attention. It attaches a value to each location in the visual field given the visual input and the current task, with regions of higher salience being more likely to be fixated. For our comparison, 11 different images captured in different conditions have been used in this study. The images used are in different times (spring, summer, autumn, and winter seasons) and different location (desert, forest, sea, urban, etc.) conditions. Experimental results show that, the mean value of the saliency measure in the 11 images are, respectively, 53% and 42% for the color similarity algorithm method and the proposed method. This indicates that the proposed method is superior to the color similarity algorithm for distinguishing the targets in their backgrounds.},  
Keywords = {Color Similarity, Digital Camouflage, Greedy Algorithm, K-means Clustering},
volume = {7},
Number = {2}, 
pages = {53-67}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-587-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-587-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Asgari, J. and Zahedi, M.},  
title = {Precipitation Prediction Using Real Time GNSS Estimated Precipitable Water Vapor}, 
abstract ={Global Satellite Navigation Systems are widely used for geodetic and geodynamics purposes. However the meteorological applications, such as Precipitable Water Vapor (PWV) estimation, are increased with GNSS permanent stations deployments all over the world. The continuity of GNSS observations and the spatial resolution of the permanent GNSS stations are some of the potentials of GNSS remote sensing using permanent arrays. In this study we are demonstrated one of the real-time meteorological applications of GNSS networks.   The spatial distribution of PWV was investigated during extreme rainfall. The PWV data from the SuomiNet network stations in the Texas state was implemented. Using linear interpolation, the PWV were determined for area within the stations. It was observed that the estimated water vapor from the GNSS observations progresses gradually towards the precipitation site and then, with accumulation in the area of ​​precipitation, the rainfall begins, and then the PWV decreases. Therefore, using a network of uniformly distributed GNSS stations, GNSS observations can be used to measure the accumulation of atmospheric precipitation in a region and to investigate the probability of rainfall occurring. These predictions will be effective if the network is sufficiently dense and the perceptible water vapor is estimated with a short latency. The estimation of zenith path delay from GNSS is possible using relative or absolute method. Furthermore the slant delay estimation is one of the possibility in the dense GNSS networks. Tropospheric tomography will aid the scientists in the future applications of GNSS. In this paper the precision of real time PWV estimation via GNSS data is investigated. French RGP GNSS networks data are used for PPP processing. The processing is performed by ultra-rapid IGS orbit and clock products and then it repeated using final IGS products. The precision of Zenith Total Delay (ZTD) of final ephemeris is about 3 mm. The Real time estimation of ZTD using ultra rapid data is compared by final solution and the RMSE for different stations are from 3 to 7 mm approximately that is sufficient for real time estimation of PWV and real time precipitations prediction. Investigation of raining occurrence and the PWV changes is performed in this paper. In the investigation of PWV it may be possible to follow a pattern or patterns for a region prior to intense rainfall, spatial variations and spatial distribution of PWV, which can predict extreme rainfall. Therefore, it is suggested that by studying the PWV behavior accurately, the probability of such patterns is examined. Also, in order to determine the accuracy of the PWV obtained from GNSS observations by the PPP method with the ultra-rapid orbit and clock products, it is possible to compare the PWV obtained from the above-mentioned method with those obtained from the measurement of radiosondes as a reliable source. Also the results of the ultra-rapid products are compared with the final IGS products the consistency is about 3-7 millimeters for the estimated ZTD values. It is also possible to predict the rainfall by the permanent GNSS stations in Iran. There are several permanent arrays which may provide the GNSS observation files instantaneously. The national geodynamic network, the Tehran's Instantaneous Network, The national cadastre RTK network and the Isfahan municipality RTK network, could be used for PWV estimation with high spatial and temporal resolution and instantaneous meteorological application of a unified network is possible.  },  
Keywords = {Zenith Total Delay, Troposphere, GNSS, Precipitable Water Vapor},
volume = {7},
Number = {2}, 
pages = {69-78}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-600-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-600-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Malian, A. and TeimuriDameski, M. and Arabi, M.},  
title = {Detection and Documentation of Kariz based on the Fusion of Aerial and Spacial Images}, 
abstract ={Documentation, preservation, maintenance and restoration of cultural heritage as well as their buffer zones are considered as important tasks of the people and the government. It is imperative for administrators and managers of civil and development projects to adhere to it. To do this, having the exact engineering maps is essential. The detailed maps are the basis for the maintenance, identification, rehabilitation and archiving of the National Cultural Heritage. Today, with the rapid growth of urbanization and the expansion of modern technologies that accelerated and facilitated construction, more attention was paid to the identification and preservation of historical monuments. A principal approach for monitoring and ensuring the preservation of the cultural heritage and its buffer zone is the scientific documentation. The main focus of the present research is on the development and design of an optimal method for the automatic detection and documentation of the Qanat (Kariz) and its environment, which is one of the engineering feats, including the unique cultural heritage of Iran, by extracting and recording spatial information. In this research, data fusion methods were used for the integration of aerial and satellite imagery in order to identify automatic water wells. Two types of integration have been made to achieve the appropriate data for Kariz detection and documentation: 1. Integration of aerial and satellite imagery; 2. Integration of the extracted features from the fused image into decision-making. Satellite and aerial images of the region in Eslamshahr have been merged into Ehler's method. After analyzing each of the different fusion methods and the histogram of the images before and after  fusing, and examination of the quantitative criteria, different radiometric characteristics of the wells of the aqueduct are extracted. These methods include applying the TC3 indices (with 62% success in identifying the desired pixels) and NDWI (with 62% success in identifying the desired pixels) and SAVI (with 52% success in identifying the desired pixels) and applying the segmentation algorithm on different image bands, the Ehler algorithm was 76% successful in identifying desirable complications. In the next step, to integrate at the decision level, two other layers of information (the layer of slope of the region and the layer obtained from the template matching algorithm with 54% success in determining the desired pixels) are extracted from the geometric properties, and along with the features obtained in the previous step, the stage of decision-making will begin. Fuzzy method has been used to integrate the results at the decision-making level. Finally, the properties of the Kariz system were detected with a better accuracy than 90%. Based on the results obtained, this method is not optimal in all conditions. Therefore, it is recommended to use the fusion method at hybrid levels and instead of using a single procedure, in each layer of information, an optimal method to input to the next step is feature extraction. Ultimately, merging the extracted properties in different layers is applied. In the present study, integration was performed at the decision-making level based on fuzzy logics. To achieve optimal performance as a result of fusion, different layers, each with their own weight, come in with different coefficients. To decide, a combination of multi-layered neural network algorithm and fuzzy logic can be used which will be tested and evaluated in the next stages of the research.},  
Keywords = {Image Fusion, Structural Similarity, Heritage Documentation, Template Matching, Fuzzy Logic, Qanat},
volume = {7},
Number = {2}, 
pages = {79-92}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-568-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-568-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {GhaffariRazin, M. R. and Voosoghi, B.},  
title = {Ionosphere Tomography using Minimization of Objective Functions Method and Neural Networks over Iran}, 
abstract ={In the last two decades, knowledge of the distribution of the ionospheric electron density considered as a major challenge for geodesy and geophysics researchers. To study the physical properties of the ionosphere, computerized ionosphere tomography (CIT) indicated an efficient and effective manner. Usually the value of total electron content (TEC) used as an input parameter to CIT. Then inversion methods used to compute electron density at any time and space. However, CIT is considered as an inverse ill-posed problem due to the lack of input observations and non-uniform distribution of TEC data. Many algorithms and methods are presented to modeling of CIT. For the first time, 2-dimensional CIT was suggested by Austin et al., (1988). They used algebraic reconstruction techniques (ART) to obtain the electron density. Since, other researchers have also studied and examined the CIT. Although the results of all studies indicates high efficiency of CIT, but two major limitations can be considered to this method: first, due to poor spatial distribution of GPS stations and limitations of signal viewing angle, CIT is an inverse ill-posed problem. Second, in most cases, observations are discontinuous in time and space domain, so it is not possible determining the density profiles at any time and space around the world.    In this paper, the method of residual minimization training neural network is proposed as a new method of ionospheric reconstruction. In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions (EOFs) are used as vertical objective function. To optimize the weights and biases in the neural network, a proper training algorithm is used. Training of neural networks can be considered as an optimization problem whose goal is to optimize the weights and biases to achieve a minimum training error. In this paper, back-propagation (BP) and particle swarm optimization (PSO) is used as training algorithms. 3 new methods have been investigated and analyzed in this research. In residual minimization training neural network (RMTNN), 3 layer perceptron artificial neural networks (ANN) with BP training algorithm is used to modeling of ionospheric electron density. In second method, due to the use of wavelet neural network (WNN) with BP algorithm in RMTNN method, the new method is named modified RMTNN (MRMTNN). In the third method, WNN with a PSO training algorithm is used to solve pixel-based ionospheric tomography. This new method is named ionospheric tomography based on the neural network (ITNN). The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN.  The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods. The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN.  The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.},  
Keywords = {Ionospheric Tomography, Total Electron Content, Artificial Neural Network, Objective Function, GPS, IRI-2012, SCH, RMTNN, MRMTNN, ITNN},
volume = {7},
Number = {2}, 
pages = {93-110}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-622-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-622-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Seydi, S. T. and Hasanlou, M.},  
title = {Fusion of Similarity and Distance based Methods for Landcover Change Detection using Hyperpsectral Imagey}, 
abstract ={Earth as the human habit, has been affected by natural events, such as tornado and flood of thunder and drought. In addition, some human activities such as urban development and deforestation have made the changes in many ways. However, these changes are unintentional they constantly threaten out the environment. So, predicting these changes are really important in order to face the consequences. Remotely sensed images, due to wide coverage, high resolution and low cost for providing data from the earth, play an important role in environment monitoring. One of the most important applications of remote sensing is change detection. Change detection is a process which measures the differences between objects in the same place at different times. The change detection is an essential tool for monitoring and managing of resources at the local and global scales. The most important criteria in chage detection are the real-time and accurate detection of land cover changes. Hyperspectral sensors operate at continuous wavelengths with a bandwidth of approximately 10 nanometers. Carrying out change detection procedures on hyperspectral images some problems appear that affect the results, such as the presence of noise in the images, and different atmospheric conditions, all of which lead to more computational complexity and an increase in execution time. This paper presents a new unsupervised change detectin method for land use monitoring by utilizing multi-temporal hyperspectral images. By incorporating similarity/distance based and Otsu algorithm in hierarchically manner, this method can detect any changes. The proposed method implements in two main phases: (1) the corrected data by using distance and similarity-based criteria that converted data to new computing space called similarity space. At this space, the changed areas can be a highlight from the no-change areas. (2) The second phase is to make a decision about the nature of pixels by a hierarchical process using Otsu algorithm that result of this phase is a binary change map. The main advantage of the proposed method is being unsupervised with simple usage, low computing burden, and high accuracy. The efficiency of the presented method has been evaluated by using Hyperion multi-temporal hyperspectral imagery. The first dataset is a farmland near the city of Yuncheng, Jiangsu Province, China. The data were acquired on May 3rd, 2006, and April 23rd, 2007, respectively. This scene is mainly a combination of soil, river, tree, building, road and agricultural field. The second study area covers an irrigated agricultural field in Hermiston City, Umatilla County, Oregon, USA. These data were acquired on May 1st, 2004, and May 8th, 2007. The land cover types are soil, irrigated fields, river, building, type of cultivated land and grassland. The results of two real datasets show high efficiency and accuracy with low false alarms rate by using proposed method compare to common change detection methods with overall accuracy of 98.48%, kappa coefficient of 0.965 and false alarms rate is 1.5% for China dataset as well as overall accuracy of 95.12%, kappa coefficient of 0.87 and false alarms rate is 4.8% for USA dataset.},  
Keywords = {Change Detection, Hyperspectral, Similarity/Distance base, Otsu},
volume = {7},
Number = {2}, 
pages = {111-126}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-546-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-546-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Shokri, M. and Sahebi, M. R.},  
title = {Fusion of Synthetic Aperture Radar Data and Optic Images based on Curvelet Transform}, 
abstract ={Satellite remote sensing (RS), gathered data with different spatial and spectral characteristics of objects or phenomena from a distance that each of them represents part of the object properties. Although multispectral data gives rich spectral information from objects, but significantly influenced by environmental factors such as smoke, fog, clouds and the sunlight. In contrast to optical sensor, the virtual aperture radar sensors have the ability to take data in all types of weather conditions or day and night. Synthetic Aperture Radar (SAR) data can highlight the structural and textural details in the image. It is sensitive to terrain components of shape, direction, roughness, and moisture.  So optical data provide detailed spectral information useful for discriminating between surface cover types, while the radar imagery highlights the structural detail in the image. Therefore image fusion techniques can help us for combining of different properties of optical images and SAR data that it can give us a complete view of the target and present higher accuracy and reliability of results obtained by this method. Curvelet transformation is more suitable in comparison with many other transformations for analysis of curved edges, high precision to approximate, describe scattering and directions. In this paper, transition SAR and optical images to Curvelet space by using Curvelet transformation, then the weighted average method is used for fusion in Curvelet space and finally fused images obtained by applying a reverse Curvelet transformation. Our case study is Shiraz city that we used data from this city for implementation of proposed method. Statistical methods and classification were used to evaluate the fused images. IHS and Wavelet transform methods is used for comparison to proposed method. Statistical parameters include standard deviation, entropy, standard spatial frequency, correlation and image quality index show improvement of fused images by proposed method than other methods. Considering that accuracy of classification depends on the image spatial and spectral information, for evaluate the effectiveness of fusion on the spatial and spectral resolution, images are classified. With the classification of input optics image and fusion image, overall accuracy improved 4 percent and Kappa coefficient increased 0.05 compared to the input image. The results show the suitability of the proposed algorithm for fusion SAR and optical images.},  
Keywords = {SAR Data, Images Fusion, Multi-Scale Transform, Wavelet Transform, Curvelet Transform},
volume = {7},
Number = {2}, 
pages = {127-138}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-504-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-504-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Aghamohammadi, H. and Behzadi, S. and Moshtaghinezhad, F.},  
title = {Developing an Analytical Model based on Spatial Statistics for Analyzing Rainfall in the Catchment Area of Lake Urmia}, 
abstract ={More than half of the world's population lives in areas where the water crisis and rainfall are serious. To cope with these crises, climatology researchers require rainfall data, pattern analysis, and rainfall estimation and management in order to manage and cope with these conditions. Iran is located in the middle belt in dry belt, which is characterized by low rainfall and high evapotranspiration. The average rainfall in the country is 250 mm and is subject to severe spatial and temporal changes. Variety of spatial factors such as position, elevation, topographic characteristics such as slope and aspect are the most effective factors in the spatial variation of rainfall. Each of these characteristics is able to determine the pattern of precipitation behavior. Therefore, in this paper, the aim is to develop a comprehensive mechanism for describing this geographical problem with the help of various earth sciences tools and techniques and considering the various environmental and spatial factors affecting rainfall. The Basin of Urmia Lake is one of the most important and most valuable aquatic ecosystems in Iran and the world. The ecosystem of this lake is a typical example of a closed basin that all runoff drain in this basin. The catchment area of Lake Urmia is selected as a case study due to the critical situation that has been facing in recent years. At first, the synoptic data of 21 stations of the Meteorological and Adventure Organization of the Ministry of Energy are used. This data is collected during the period of 63 years of statistical period from 1951 to 2014, and then the annual precipitation rates of the stations are calculated as the dependent variable based on these statistics. In addition data, longitude, latitude, height and slope of each station as well as the average annual and average annual wind speed were also extracted as independent variables. First, initial statistical tests (rainfall data normalization at stations, data normalization, trend review and deletion) were performed. Then, a combination of traditional and statistical methods are reviewed and examined. As a result, the ordinary Kriging method was selected with RMS equal to 4.15. Then, with the help of different analytical and spatial methods, including cluster analysis, the southern and southwestern regions of this lake as hot and high-frequency parts as well as low-low cold spots in the northern and central parts of the basin Lake Urmia and two spots in the Sarab and Salmas areas with low concentration of rainfall were identified in this area. At the end, in order to model spatial relationships, general regression was fitted to rainfall, and the latitude is obtained as the most effective dependent variable. In addition, longitude and wind speed are detected as the least effective variables on precipitation in the lake of Urmia. The results of this paper have shown that land survey methods are more accurate than traditional methods for locating Lake Urmia.},  
Keywords = {Rainfall estimation, Interpolation, Geo-statistics, Spatial Relationships Modeling, Cluster Analysis},
volume = {7},
Number = {2}, 
pages = {139-151}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-631-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-631-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {AkbariDotappehSofla, R. and AkhoondzadehHanzaei, M.},  
title = {Development of a Split Window Model to Retrieve Land Surface Temperature Map from Thermal Hyperspectral Images}, 
abstract ={Land surface temperature (LST) is among the most important indices in the studies related to earth surface such as conservation, energy exchange and the water between the land surface and atmosphere. The main goal of this study is to present an algorithm in order to estimate the land surface temperature using the data of the Hyperspectral Thermal Emission Spectrometer (HYTES). The HYTES sensor has 256 bands in the range of 7.4-12 micrometers that bands in the range of 7.4-8 micrometers are removed due to strong water vapor in this spectral region and bands above 11.5 micrometers are removed due to issue of calibration. 202 bands remain, which we want in this study to obtain optimal bands from 202 bands using the genetic algorithm and then obtain the land surface temperature using those bands. We need to define a cost function and appropriate initial parameters for the genetic algorithm to select optimal bands. In this research, the cost function is to minimize the temperature difference between the thermal product of the sensor and the obtained land surface temperature with a split window algorithm and the number of variables in each gene is as large as the number of bands (202) and the initial population is 80 in genetic algorithm. The bands used in split window algorithm are selected using the genetic algorithm. In this study, we use split window algorithm that obtain land surface temperature through optimal bands that are selected using Genetic algorithm among 202 bands. Generally, in this study, first we use Genetic algorithm to choose optimal bands from 202 bands and obtain the coefficients of split window algorithm.The number of these coefficients is dependent on the number of bands which are selected by Genetic algorithm. Then, by using resulted coefficients that are obtained with least squares method and selected bands, land surface temperature is obtained for two different data through split window algorithm. In this research, a small part of the first data was used as training data for the genetic algorithm to obtain the coefficients algorithm of the split window and optimal bands to calculate the land surface temperature for the rest of the data. In a separate window algorithm, in addition to the algorithm coefficients, we need the emissivity of the relative bands used in split window algorithm. In this study, we used the emissivity product of the HYTES sensor. Among 202 bands, 110 bands are selected using the genetic algorithm. Using this 110 bands, split window algorithm coefficients and bands emissivity, land surface temperature is calculated for two data and evaluated. Finally, the thermal product of HYTES is used to evaluate the our proposed method and indicate its accuracy. The temperature obtained using proposed algorithm for both data is evaluated with reference data (thermal product) and the RMSE value is resulted as 0.025 and 0.999 for the first and second data respectively. Therefore, according to the obtained errors, we can argue that the proposed algorithm is an appropriate method to obtain the land surface temperature using the data of HYTES.  },  
Keywords = {Land Surface Temperature, Split Window, Genetic Algorithm, HYTES},
volume = {7},
Number = {2}, 
pages = {153-165}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-643-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-643-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Hemmati, Z. and Ebadi, H. and HosseiniNavehAhmadabadian, A. and Esmaeili, F.},  
title = {Presented a New Algorithm for Network Design and Path Planning it Captures Drone Modeling Purposes Archaeological Sites}, 
abstract ={3D models of ancient sites are produced and utilized for different purposes such as research, restoration and renovation of valuable ancient objects, creation of virtual museums and documentation of ancient sites. Nowadays, Geomatics techniques, as the most efficient methods for geometrical measurements, analysis and interpretations concerning issues in cultural heritage, are applied to produce geometric and thematic information. Buildings are susceptible  to change and damage through the passage of time due to natural agents and disasters such as rain, wind, earthquake, flood, or damages imposed by human beings. The characteristics of these changes in some buildings with ancient value bear special importance. The first step to create 3D models, provide the information about ancient monuments and record them with documents is having accurate maps of their present condition to be able to add other information like type of construction materials. Special techniques should be employed to provide maps with high accuracy, in addition to other characteristics such as spending the least expense and time for continuous map production. The process of changes are recognized by comparing maps from different time spans based on which due decisions can be made. To provide these maps many different techniques have been used since past such as traditional surveying (using the usual total stations), photogrammetry (especially close-range photogrammetry) and laser scanners. In comparison to other techniques, photogrammetry has unique characteristics in documentation of ancient sites. No need to contact with the feature, the possibility to obtain the information of texture and color and the compliance of these characteristics with the 3D output data, high flexibility of this method to access the desired accuracy in measurements and its potential of access to accuracy at micrometer level as well as capability of low expense observations and archiving images, are parameters that have given rise to the more usage of techniques of photogrammetry in the modelling of ancient sites. Yet, the usual techniques of photogrammetry sometimes have limitations, for example, in rare cases of inaccessible features. As a result, the requirement to obtain accurate information from features, especially in dangerous and remote areas, and also, the necessity to economize expense and time have led to the usage of UAV-based photogrammetry. UAV-based photogrammetry is a combination of aerial photogrammetry and close-range photogrammetry in which there is a sensor that can be a metric or non-metric camera or any other data collection tool. The images are acquired from low height. Access to imaging stations with appropriate angle toward all parts of a feature and low height of flight, result images with high spatial resolution, which consequently, bring about more accurate and precise 3D information from earth. Different categorizations have been presented for UAVs based on different criteria and applications. To mention some of these criteria we can refer to the criterion of flexibility, fixed or rotating blades or wings in UAVs and their source of energy. Based on the categorizations of platforms regarding this research, which is ancient sites, it is obvious at first glance that UAVs with fixed wings, fixed or semi-flexible parachutes and wingless are practically of no use due to low flexibility in flying  and imaging , and also limited space of flight. Therefore, low expense, high flexibility and appropriate time of flight have contributed to the suitability of quadrotors as the best option among all systems with rotating blades in this research. Low expense for production, no need to airports and long runways and better maneuverability are some particular parameters and characteristics of the functionality of UAVs. There are factors that limit the function of UAVs, such as instability while flying due to light weight, limited source of supply, limitation to carry bigger and more accurate measuring tools and requiring longer time for imaging, processing and calculations. Fortunately, all these limitations can be modified to some extent by an appropriate network design. In spite of all aforementioned capabilities of UAV systems, no specific standards have been designed to utilize them. Therefore, it is obviously necessary to investigate the feasibility of the usage of these systems, and to design appropriate networks to locate them in proper points to obtain images for photogrammetry. Thus, the need for high accuracy in UAV-based photogrammetry for documentation and restoration of ancient sites necessitates more concern for the network geometry to achieve the desired accuracy. This article presents appropriate method for optimum locations of UAV for imaging. The proposed method for the optimal locations of UAV is based on the ellipsoid fitted on object, principles and constraints of photogrammetry network design and finally by exploring hidden areas. The results from images taken from a cultural heritage site showed that the number of images was reduced almost 4 times by applying network design principles. Consequently, the speed of 3D modelling would be increased almost eight times by applying the proposed method.},  
Keywords = {UVA-based Photogrammetry, Network Design, Path Planning, Modelling of Ancient Sites},
volume = {7},
Number = {2}, 
pages = {167-180}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-588-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-588-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Karami, A. and Varshosaz, M. and Soryani, M.},  
title = {Using Textural Features in Frame Subtraction Method to Remove Car Shadows}, 
abstract ={Vehicle detection is a significant step for many applications such as automatic monitoring of vehicles, traffic control, transportation, etc. In these systems, a fixed camera is usually installed in some places such as highway, street and parking. Images captured by the camera firstly will be processed, and then the vehicles in it will be identified. Generally, in these systems, cars are the favourite’s parts in a video frame. Therefore, providing methods that can increase overall accuracy and also the efficiency in vehicle detection is very important. Several methods have been proposed by researchers to detect cars in images. Frame subtraction is one of the most known in which every change between the current frame and the background image is regarded as a moving car. Since car shadow is identified as variable pixels, so there is a connection between the shadow of a car and that car, which causes two problems in these systems: Firstly, the actual shape and appearance of the car can disappear because of this conjunction. As a result, it is difficult to detect the location of the vehicle and to segment the image based on cars. Secondly, the shadow of one or more cars may interfere, and all of them are identified as a vehicle. These items cause many problems in monitoring systems and traffic control systems such as car counting, or vehicle location estimates and behaviour analysis. So shadows need to be identified. To solve this problem, recently, Karami has developed a method based on the region growing to remove the shadow of the vehicle. First of all, we should find the seed point, and then, based on the Euclidean distance, we will consider the distance from the seed point to each pixel of the image in the feature space as a weight. We will use these weights to separate the shadow of the car. The main problems about that are the complexity and computational speed, low precision and also a strong dependency on grey-grade changes. Therefore, in this research, we want to solve the above problems by improving the method of Background subtraction by weighting the pixels of the image by combining several texture features to remove the shadows of vehicles. To do this, each pixel in the background image and the current frame is weighted based on a combination of textural features. This makes the shadows and the background (asphalt) pixels to have very close values and thus removed in subtraction. The proposed method is evaluated on four datasets based on OA, HR, FAR, MODP and MOTP criteria. Using these criteria, the proposed method is compared and evaluated with region growing method, median and averaging methods, and several other methods in shadow detection. However, in general, according to the results, the proposed method outperforms than other algorithms by 2 to 15 percent improvement in mentioned criteria. To continue and complete the research in the field of vehicles detection and remove of shadows recommended: Using several different data, which are obtained in different situations and at different times. 2. Using other non-statistical colour and texture features. Detection of vehicles based on geometric structure},  
Keywords = {Car Detection, Shadow, Weighting, Textural Feature},
volume = {7},
Number = {2}, 
pages = {181-199}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-632-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-632-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Zareei, A. and Emami, H.},  
title = {A New Model for Forecasting Recovery Period of the Urmia Lake Water Level and Assessment of Spatiotemporal Changes of its Stabilization Using Remote Sensing}, 
abstract ={Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. In this study, the surface area changes of Lake Urmia, Iran were investigated. Lake Urmia, with an area varying from 5200–6000 km2 in the 20th century, is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. The lake is the habitat for a unique bisexual Artemia (a species of brine shrimp), and becomes a host for more than 20,000 pairs of Flamingo and about 200–500 pairs of White Pelican every winter. Lake Urmia forms a rare and important ecologic, economic and geo-tourism zone and was recognized as a Biosphere Reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 1975. In addition, the lake helps moderate the temperature and humidity of the region, providing a suitable place for agricultural activities. Assessment and monitoring of Lake water level changes, in order to protect them in terms of importance, nature, and location are considered at the national, regional and global levels. However, in recent years to improve the water level in the lake, activities have been carried out but unfortunately due to a decrease in the water level of the lake is in a critical state. Therefore, it is necessary the increase or decrease of Urmia Lake water level and its impact on the environment to be monitored on a regular basis and provided correctly scheming and decision-making to effectively improve its situation. In this research, a new model for forecasting recovery period (compared to 14 years ago) of the Urmia Lake water level is conducted and assessment of spatiotemporal changes of its stabilization using  Landsat 5 and 7 and 8 multi-temporal imageries during the period 2002 to 2016. In the proposed model have been considered two main factors, average annual rainfall catchment area and the activities taking place in recent years. To this end, to assess spatial-temporal changes of Urmia Lake water level, four different water body extraction indices, including water ratio index (WRI), automatic index extraction of water (AWEI), normalized difference Water Index (NDWI) and the normalized difference vegetation index (NDVI) were used. Then, the performance of each of the four indices was compared with a base map and it was determined the error of each index that NDWI was the lowest error compared to the other three indices. As a result, the proposed model was presented based on the results of this index in three different state, taking into account the various weights to previously mentioned factors. The results showed a marked reduction (78%) of the Urmia Lake water level occurred in the period 2002 to 2014 compared to 2002. In contrast, from 2014 to 2016, the Urmia Lake water level has increased 57.33 % and it has been reached the relative stability condition. This relative stability is unstable and depends on two main previously mentioned. In addition, the results of the proposed model in three different state are shown that based on the increasing trend in the second period and taking into consideration the different weight of the main factors. It will take up at least 11 years (at best), 18 years (the status quo) and maximum 49 years (reduced recovery activity) the Urmia Lake water level returned to its original level in 2002 and it achieved a stable condition. The proposed model is a suitable method and it can be used for any number of recovery activity factors in the future.},  
Keywords = {Urmia Lake, Remote Sensing, Landsat, Spatio-Temporal Changes, Water Body Extraction Indices},
volume = {7},
Number = {2}, 
pages = {201-214}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-626-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-626-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Gudarzi, S. and Alesheikh, A. A. and Honarparvar, S.},  
title = {Devloping a Recommander Location Algorithm Utilizing Temporal Influence, Geographical Influence and Social Influence}, 
abstract ={Social network-based services  are among the services that have been welcomed by smart phone users. Location recommendation is a popular service in social network.  This  service suggested the unvisited places to the users based on the is based on the users’ visiting histories and location related information such as location categories. The existing methods  which utilize check-in data and category information, only consider temporal and spatial information. Since the influence of user’s social relations can play an important role in location recommendation since it can improve the algorithm performance. In this paper, a PCLRSGT method is developed that consider temporal, spatial and social components. The spatial component models a user’s probability of checking in to a location. The spatial model obtains user’s home location by using his check-in dataset. It calculates the distance from the location to the user’s home. The spatial PDF filters out those locations that are far away from a user’s home and are not in the users’ interest. These locations should not be recommended to the user. The temporal component employs similar users’ check-in probabilities to model a user’s probability of checking in to a location. It constructs users’ temporal curves to represent a user’s periodic check-in behavior. A User Temporal Curve U for category j is defined as a sequence of probability values. The probability value is denoted as  that means probability of checking into category j in hour m (1≤m≤24).The probability sequence for user u into category  is denoted as .  Since the distances between users temporal curves are used to find users’ similarity, in this paper the distances are measured by curve coupling method. Temporal similarity is used to predict user’s probability of checking in to a location. The periodic behavior of a certain user is predicted by a weighted summation of the periodic behaviors of his similar users. If two users are more similar in terms of temporal similarity, they influence each other's periodic check-in behavior more. The social component models a user’s probability of checking in to a location by considering similarity between user and his friends in terms of social connection, periodic check-in behavior and check-in activities into locations. The social influence weight between two friends is concluded based on all three similarities between a user and his friends in terms of social connection, periodic check-in behavior and check-in activities into locations. Therefore, the social influence weight between two friends is calculated by combining the three above factors. The social influence weight between two friends is used to predict user’s probability of checking in to a location. The dataset employed in this paper was collected from Gowalla. Gowalla was one of the popular location based social network launched in 2007 and closed in 2012.  .The data set contains 1000 users and 15905 check-in records. A check-in record indicates a user has visited a location at a given time. It contains the user ID, location ID, and time stamp of the check-in. To evaluate the performance of the recommendation algorithm, the dataset was divided into training and testing datasets. So, one of the check-in records of each user was randomly moved to the testing dataset. The rest of the dataset formed the training dataset. As the result, the testing dataset contained 1000 check-in records, and the training dataset contained 14905 check-in records. In this paper, Precision and Recall were used to evaluate the performance of the location recommendation algorithm, which are widely accepted as the performance measurement for recommender systems.   The performance of the proposed recommendation algorithm is compared with two existing location recommendation methods, Probabilistic Category-based Location Recommendation (PCLR) and Probabilistic Category-based Location Recommendation Utilizing Temporal Influence and Geographical Influence (sPCLR). The performance of proposed algorithm is reported by recommending top-N recommendation list in the testing set (N=1, 2, 5, 10, 15 and 20). Experimental result prove that PCLRTGS performed better than all other algorithms, in terms of both precision and recall about 10 to 15 percent. This proved that using the social influence helps to improve the location recommendation. It can also be observed that the precision value decreases when the number of recommendations increases. There are two reasons for the precision decreasing, the number of correct recommendations decreases when the number of recommendation increases, or the number of correct recommendation increases with a lower rate compared to the number of recommendations. We should check the recall values to see which one is true. It also is seen that the recall values increase as the number of recommendations increases. Since the number of correct answers is a constant value, so it can concluded that the number of correct recommendations is increasing when the number of recommendations are increasing.  },  
Keywords = {Location-based Social Network, Location Recommendation, Temporal Curve, Social Influence},
volume = {7},
Number = {2}, 
pages = {215-230}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-633-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-633-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Sedaghat, A. and Mohammadi, N.},  
title = {Reliable Image Matching Based On Hessian-Affine Detector and MROGH Descriptor}, 
abstract ={Reliable image matching is a vital step in many photogrammetric processes. Most image matching methods are based on the local feature algorithms because of their robustness to significant geometric and radiometric differences. A local feature is generally defined as a distinct structure with properties differing from its immediate neighbourhood. Generally, local-feature-based image matching methods consist of three main steps, including feature detection, feature description and feature correspondence. In the feature detection step, distinctive structures are extracted from images. In the feature description step, extracted features are represented with descriptors to characterize them. Finally in the correspondence step, the extracted features from two images are matched using particular similarity measures. n this paper, an automatic image matching approach based on the affine invariant features is proposed for wide-baseline images with significant viewpoint differences. The proposed approach consists of three main steps. In the first step, well-known Hessian-affine feature detector is used to extract local affine invariant features in the image pair. In Hessian-affine detector a multi-scale representation and an iterative affine shape adaption are used to deal with significant viewpoint differences including large scale changes. To improve the Hessian-affine detector capability, an advanced strategy based on the well-known UR-SIFT (uniform robust scale invariant feature transform) algorithm is applied to extract effective, robust, reliable, and uniformly distributed elliptical local features. For this purpose, a selection strategy based on the stability and distinctiveness constraints is used in the full distribution of the location and the scale. In the second step, a distinctive descriptor based on MROGH (Multisupport Region Order-Based Gradient Histogram) method, which is robust to significant geometrical distortions, is generated for each extracted feature. The main idea of the MROGH method is to pool rotation invariant local features based on intensity orders. Instead of assigning a main orientation to each feature, a locally rotation invariant schema is used. For this purpose a rotation invariant coordinate system is used to compute the pixels gradient. To compute descriptor, the pixels in the feature region are partitioned into several groups based on their intensity orders. Then, a specific histogram based on the pixels gradient magnitude and orientation is calculated for each group. Finally, the MROGH descriptor is generated by combining the values of all the gradient histogram from all groups into a single feature vector. Finally, feature correspondence and blunder detection process is performed using epipolar geometry based on fundamental matrix. The initial matched features that are not consistent with the estimated fundamental matrix are identified as false matches and eliminated. A distance threshold TE = 1 pixel between each feature point and its epipolar line is considered as elimination condition. The experimental results using six close-range images show that the proposed method improves the matching performance compared with several state-of-the-art methods, including the MSER-SIFT, UR-SIFT and A-SIFT, in terms of the number of correct matched features, recall and positional accuracy. Based on the matching results, the proposed integrated method can be easily applied to a variety of photogrammetric and computer vision applications such as relative orientation, bundle adjustment, structure from motion and simultaneous localization and mapping (SLAM).  },  
Keywords = {Image Matching, Hessian Affine, MROGH, Covariance Matrix, Close Range Images},
volume = {7},
Number = {3}, 
pages = {1-15}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-623-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-623-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Javaheri, F. and Saadatseresht, M.},  
title = {Assessment Methods SPS, RLS And MP In The Calibration MEMS_IMU}, 
abstract ={History inertial measurement unit (IMU) dates back to 1930. At that time, the technology because of its serious limitations on size, cost and power consumption, and the public was not applicable to small devices. Recent developments inertial measurement unit of MEMS sensors that are lighter, smaller and cheaper than other types of systems are inertial. These have made the inertial sensors in a variety of small devices such as mobile phones placed today. In this article, the first to introduce a variety of inertial systems and focus more on MEMS systems has been based on the mathematical model used where errors bias, scale factor and non-orthogonal axes is located. In summary, each of these errors can be expressed as follows. bias is the measurement error, regardless of the forces or the input rate of the sensor. The scale factor expresses the scale of the relationship between the output of a sensor with the force or the rate applied to the sensor. In other words, this error shows the difference between the ideal observation and the output of the sensor, and linear gradients are generated in observations in the inertial navigation systems. non-orthogonal axes between the axes is a defect in the construction and a lack of alignment of the three axes of measurement in the accelerometer or gyroscope. The following guidelines are provided to calibrate the static inertial systems. The methods by six static position (SPS), multi-position (MP) and recursive least squares (RLS) is an IMU used to determine the parameters of error. In this research, OARS software is used, a data aggregation system developed on the Android operating system. With 100Hz, this software provides accelerometer, gyroscope, magnetometer and GPS observations at 1 Hz. Observations of these sensors are stored in the csv format software in the phone's memory. In this study only gyroscope and accelerometer sensors are used. The results showed that the proposed methods First, the mathematical model with 12 parameters high dependency between the parameters compared to model 9 parameters. Secondly, if the raw observations in a particular situation, and the values averaged better results than entering into the equation raw observations or observations been removed noises by discrete wavelet transform. The unit of measurement error values in the multi-position on the three modes obtained by 0.082345, 0.083140 and 0.082952  is. Thirdly, the results of the error measurement unit in three ways proposed by 0.146245, 0.161520 and 0.082345  and because of the need to collect data in particular (exact alignment inertial system) to determine the calibration parameters show that the multi-position method is better than the other two methods.},  
Keywords = {Calibration, Inertial navigation systems, Accelerometers, Gyroscopes, DWT, Android},
volume = {7},
Number = {3}, 
pages = {17-28}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-575-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-575-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Azizi, A. and Hadiloo, A. and Sharifi, M. A.},  
title = {Accuracy Evaluation and Analysis of a Localized Geometric Transformation Approach for Two Dimensional Landslide Displacement Measurements Using IRS P5 Satellite Images}, 
abstract ={Determination of the displacement vectors due to the landslide phenomenon is the initial stage for generating landslide inventory and susceptibility maps. The main objective of this paper is to evaluate the achievable accuracy of a simple two-dimensional localized geometric transformation method for the determination of the landslide displacement vectors. For this purpose, two IRS P5 backward images taken in two different revisit epochs over the Ardabil Province are used. The main objective of this paper is to compare the acquired image based displacement vectors with the ground measurements. The 2D localized transformation approach may be utilized to produce a small and medium scale country-wide landslide inventory maps and for continuous monitoring of the areas susceptible to large landslides. With regard to the fact that the coverage of the landslide zones in satellite imageries are usually small as compared with the entire image frame, the achieved accuracy figures suggest that the localized transformation approach may fulfill the required demands. Onsite displacement measurements performed by the GPS receivers, conducted by the ISTA SANJ DAGHIGH Co, were utilized to assess the accuracy of the localized transformation method. To compare the displacement vectors generated from the images with the ground observations, spatial registration between the points measured on the image and the points measured on the ground was conducted using the satellite supplied RPCs through which the ground coordinates for the measured image points are calculated. However, due to the fact that the supplied RPCs have a systematic shift error, to generate the ground coordinates for the measured image points, it is necessary to eliminate the systematic shift of the RPCs. This is achieved by identifying fixed feature points (outside the landslide zone) on the stereo P5 images for which ground observation were available through the large scale map of the area. The RPC shift error is then calculated by comparing the ground coordinates of the fixed points generated by the RPCs and their corresponding ground coordinates extracted from the large scale map. On the other hand, due to the lack of coincidence between the dates of the image acquisition and the ground observations, a temporal interpolation was also necessary. The temporal registration was performed using a linear interpolation approach assuming a linear land displacement. The average landslide speed on the ground was adopted as the amount of linear displacement trend for the period over which the assessment was conducted. The outline of the evaluation approach and a detailed description of the test results and the final accuracy figures are presented in this paper. },  
Keywords = {Landslide, Localized Geometric Transformation, Accuracy Evaluation, Temporal Registration, Spatial Registration, IRS P5 Backward Images},
volume = {7},
Number = {3}, 
pages = {29-38}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-377-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-377-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Maleki, J. and Hakimpour, F. and Masoumi, Z.},  
title = {Ranking and Determining Change-Prone Urban Land-Uses by Fuzzy Multi-Criteria Decision Analysis}, 
abstract ={Urban land-use allocation is a complicated problem due to diversity in land-uses, a large number of parcels and different stakeholders with various and conflicting interests. A variety of studies presents many criteria in this regard. Different methods have been proposed for the optimal allocation of urban land-uses. The outputs of these methods are near optimum layouts that offer a suitable land-use for every land unit. However, because of some limitations such as disagreement of stockholders by a specific land-use or the high cost of land-use conversion to a certain land-use, it is not possible for planners to propose desirable land-uses for all parcels and as a result, have to use next priorities of the land-uses. Thus, prioritizing land-uses for parcels along with optimal land-use allocation could be essential in urban land-uses planning. Furthermore, due to the approximate nature of land-use evaluation criteria, using fuzzy calculations can be more compatible with the urban land-uses allocation models. Therefore, in this study, a parcel-level urban land-use prioritization model based on fuzzy calculation is presented. In the proposed model, at first evaluation criteria are estimated by fuzzy calculations for each parcel. Urban land-use evaluation criteria include neighborhood effects (i.e. compatibility, dependency, and proximity), physical suitability, and per capita. Compatibility and dependency factors depend on the different service level of each land-use. Each land-use is defined in three service level of local, district and regional and different radius of effect is considered according to these service levels. Furthermore, suitability criterion is calculated according to the characteristics and physical properties of land units for each land-use as a fuzzy number. Per-capita criterion is calculated as per capita violation in a fuzzy manner and is considered in land-use prioritization. After fuzzy calculations of criteria, the importance of each criterion must be determined. To determine the importance of each criterion in proposed model, the weight of criteria is estimated by subjective and objective weighting approaches. Expert knowledge is used for estimating subjective weights, and Shannon's entropy method applied for determining objective weights. The Fuzzy TOPSIS[1] technique is used to prioritize land-uses for each parcel. In fuzzy TOPSIS, after normalization and applying weights to each criterion, positive and negative ideal points are calculated based on the best and worst values of criteria. Finally, with calculating the distance of each land-use as alternatives from worst and best ideal points, land-uses will be ranked for each parcel. This procedure is repeated for all parcels in the study area, and therefore, all land-uses are ranked for all parcels. The proposed model was implemented on spatial data of region 7, district 1 of Tehran. Ranking urban land-uses for parcels in the study area showed that 77.2 percent of current land-uses have the first priority for their own parcels. It came that 22.8 percent of parcels in the study area were not allocated the first priority of land-use, and the land-use of these parcels can be susceptible to change. In the land-uses of the study area, in terms of susceptibility to changes, residential units have the best situation, and industrial units have worst situation. As a future research, based on proposed model, different scenarios can be proposed for optimal allocation of urban land-uses by taking into account stockholders' preferences. For modeling the stakeholders' preferences, approaches such as multi-agent systems and game theory can be used.   [1] Technique for Order of Preference by Similarity to Ideal Solution},  
Keywords = {Urban Land-Use Prioritizing, Urban Land-Use Planning, Multi-Criteria Decision Analysis, Fuzzy Arithmetic, Fuzzy TOPSIS},
volume = {7},
Number = {3}, 
pages = {39-55}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-609-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-609-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Mohammadinia, A. and Alimohammadi, A. and Ghaemi, Z.},  
title = {Evaluation and Comparison of Performance of Fixed and Adaptive Kernels in Geographically Weighted Regression for Modeling Leptospirosis in Gilan}, 
abstract ={There are more than 200 types of zoonotic diseases in the world and leptospirosis is the most important. Leptospirosis occurs mostly in areas with a tropical climate and abundant rainfall. There are no specific statistics of this disease worldwide, and records are underestimated for several reasons. Hence, the World Health Organization (WHO) named leptospirosis as a neglected tropical disease in the world and more research is needed in this field. When paddy season in the north of Iran begins, the disease spread and in severe cases leads to death. Leptospirosis is recognized globally as a multi-faceted disease and failure to recognize or treat it onetime can lead to death of patients. The main cause of the spread of this disease is a bacterium present in the body of domestic and wild animals, especially mice and dogs (as reservoirs of disease) and transmitted through the urine or feces to the environment. As a result, the bacteria can be transmitted to the human body through injuries to the skin or contact with contaminated soil and water. The environment and occupation are very effective in the spread of the disease, which is recognized as a work-related illness and can be dangerous in both urban and rural areas. The emergence of this disease can be due to reasons such as agriculture, livestock, butchers, recreational activities and water sports, poverty, travel to tropical areas, and any activity that leads to contact with water, soil or contaminated environment. This disease is more prevalent in fishermen and farmers, especially sugar cane farmers and workers, and it is very important to cause problems such as inability to work properly in the time and season needed planting and harvesting, as well as medical costs and even mortality. Compared to other provinces, Guilan province has the highest rate of leptospirosis recently. Therefore, the study and modeling of this disease in the province is of great importance. In this paper, the disease statistics in the rural area during 2009-2011 were assessed as the dependant variable and five variables considered as independent variables for modeling spatial distribution. Considering the important effects of bandwidth and weighting function on modeling results, the efficiency of fixed and adaptive kernels, Bi-Square and Gaussian weighting functions investigated. Two criteria were utilized to evaluate the results include MSE and Definition Coefficient. The results showed that the adaptive kernel and Bi-Square performed better than the fixed kernel and Gaussian, respectively. In terms of bandwidth selection criteria, AIC, CV and BIC played more meaningful role consecutively. Among the environmental variables, Temperature, Humidity and Evaporation illustrated positive relationship with disease and, elevation and slope showed negative relationship. The maps of the distribution of the disease indicated that the central regions of Guilan province are more prone to this disease than the other areas, and management and control of the disease in these areas is very important. Finally, all results were assessed by validation criteria and decision makers can use this helpful information for prevention programs and allocation of budget to the risky areas.},  
Keywords = {Infectious Disease, Leptospirosis, GIS, Spatial Analysis, GWR},
volume = {7},
Number = {3}, 
pages = {57-73}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-598-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-598-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Khanbani, S. and Mohammadzadeh, A. and Janalipour, M.},  
title = {Global and Local Change Detection Using K-Means Clustering Improved by Particle Swarm Optimization}, 
abstract ={Change Detection (CD) considered as an important issue among researchers due to its applications in different aspect like urban management, environmental monitoring, and damage assessment and so on. Different methods and techniques have been proposed for CD process. One of the most common categories presented in the field of CD is supervised and unsupervised techniques. Unsupervised CD techniques are based on image information and do not require any additional information including training samples. Several studies have been done for unsupervised change detection methods. Some of the proposed algorithm are based on clustering technique and considered two cluster centers for entire image. It can be critical because changed and unchanged pixels might not be present consistent behavior at entire image so it can be conducted misclassification of changed and unchanged pixels. Another method considered clustering process at block levels of image. So changed or unchanged pixel might not be present at all block level of image simultaneously, it can be leaded misclassification of above mentioned pixels. In this paper, a novel unsupervised CD method is proposed based on K-Means clustering algorithm improved by particle swarm optimization method (PSO) to solve above mentioned problem. The proposed method comprises five main steps including: 1-preprocessing (radiometric and geometric correction), 2-generation of difference image and feature extraction (neighborhoods pixels information), 3- split of difference image into non-overlapping block (block analysis), 4-the proposed PSO-K-Means clustering and create binary change map, and 5-accuracy assessment (i.e. absolute and relative accuracy assessment). The main goal of the proposed PSO-K-Means method is an automatic detection of change area which occurred between bi-temporal remote sensing images. In the most region, there is different spectrum of changes, so the aim of proposed method is detecting the spectrum of change area at block level and also preserving global image information. To achieve the mentioned aim a novel cost function which considered K-Mean clustering at block level and at entire image simultaneously presented in this paper. To find optimum clustering centers with the minimum cost or in other word finding optimum feature vector corresponded to optimum cluster centers (changed and unchanged clusters), the PSO method was employed. Three cost function comparisons were implemented in order to verify the necessity of the proposed cost function. Moreover, maximum voting method applied in order to combine different band change maps to improve CD result. Finally, a sensitivity analysis was employed in order to confirm the validation of the proposed PSO-K-Means method. The sensitivity analysis employed against different block size, different initial population and iteration in the optimization process. The results show the stability of proposed method against initial population and iteration parameters. The proposed algorithm showed sensitivity against changing block size of image. Experiments applied on two data sets (i.e., Alaska and Uremia Lake). Both Data sets acquired by the Landsat satellite with seven spectral bands and 30 meters pixel size with the same image size (400 × 400 pixels). The ground truth image was created manually by an expert in visual analysis of the input images. The proposed method improved change accuracy 8%-12% rather than common methods, (i.e. FCM, Otsu thresholding, K-Means, K-Medoids) in both Alaska and Uremia. The optimum block size determined using experimental result.},  
Keywords = {Unsupervised Change Detection, PSO-K-Means Clustering, Local Change Detection},
volume = {7},
Number = {3}, 
pages = {75-88}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-650-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-650-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {KarimiFiruzjaei, M. and KiavarzMoghadam, M. and Mijani, N. and AlaviPanah, S. K.},  
title = {Quantifying the Degree-of-Freedom, Degree-of-Sprawl and Degree-of-Goodness of Urban Growth Tehran and Factors Affecting it Using Remote Sensing and Statistical Analyzes}, 
abstract ={Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. The analysis of urban growth using spatial and attribute data of the past and present, is regarded as one of the basic requirements of urban geographical studies, future planning as well as the establishment of political policies for urban development. Various methods have been used to investigate the physical growth of cities up to now. The remote sensing method and geographical information system are the most updated, precise and economical methods used to investigate the physical growth of cities. Since physical expansion occurs in temporal and spatial scale, the built-up land use can be extracted by using remote sensing multi-temporal data. Then, by comparing these data in different time periods by using statistical and spatial analysis of geographical information system, amount, and the ratio of changes were evaluated and its trend was modeled to be used in the urban planning. In this study six temporal satellite images of 44 years interval (1972, 1984, 1992, 2000, 2008 and 2016) have been classified to determine the urban extent and growth of Tehran in 8 different geographical directions within a circular region. So as to analyze data, Pearson’s chi-square statistics, Shannon’s entropy model and degree of goodness index were utilized. Pearson’s chi-square statistical model determines the amount of urban growth difference in various time periods which can be used along with Shannon’s entropy model to determine changes and scattering in the expansion of urban boundaries. The degree of goodness index can be used to investigate the urban growth quality. In the last studies, these models have been used to analyze spatial phenomena of the city such as changes trend in the city structure and shape in order to spatial expansion and land use changes. In this study, the used method of analyzing spatial data completely differs from the literature. In this model, a prediction model of CA-Markov is used to anticipate the urban growth in the year 2024 and also statistical parameters such as Shannon’s entropy and Pearson’s chi-square are used to analyze the way, the amount and the degree of goodness of the urban growth in the past and future. In this way, it was found that the city of Tehran has a high degree-of-freedom, high sprawl, and a negative goodness in urban growth. The total sprawl equals to 4.71 which is dramatically higher than half of  value. As a result, it can be generally inferred that the city experienced a high sprawl value during 1972-2016 and this trend would continue to next years. The results indicate that in various time periods the city does not experience a good urban growth. The investigation of the degree of goodness index in various sectors also follows the same trend as various time periods. Nonetheless, different sectors and time periods can be compared to each other. Sectors of North and NorthEast compared to the other sectors, have the best and the worst urban growth, respectively. This study establishes the foundation for an in-depth recognition of urban expansion in Tehran and optimization of future urban planning.},  
Keywords = {Degree-of-Freedom, Degree-of-Sprawl, Degree-of-Goodnes of Urban Growth, Remote Sensing, Statistical, Tehran},
volume = {7},
Number = {3}, 
pages = {89-107}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-584-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-584-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Abdi, N. and AzmoudehArdalan, A. R. and Karimi, R.},  
title = {Combination of GPS and Satellite Altimetry Observations for Local Ionosphere Modeling Over Iran}, 
abstract ={Ionosphere is the upper part of the atmosphere that extends from 80 to 1200 km above the Earth’s surface. The existing free electrones and ions in the ionosphere layer affect the signal propogation speed such as satellite positioning and satellite altimetry signals. Regardless of the fact that Dual frequency measurments can remove ionospheric delay effect, dual frequency observations of the permanent GPS stations can also be utilized to produce the ionosphere maps including the vertical total electron content  (VTEC) values. For instance, International GNSS service (IGS) sub-centers produce daily global ionosphere maps (GIMs) using the GNSS data. The spatial resolution of GIMs in the latitude and longitude directions is 2.5 degree and 5.0 degree, respectively, and their temporal resolution is 2 hours. One of the IGS sub-centers, namely CODE produces the GIMs based on the spherical harmonic basis functions up to the degree and order 15. The aim of this research is to develop a local inosphere model based on the B-spline basis functions using the combined GPS and satellite altimetry observations over Iran. Accordingly, the potentiality of the B-spline basis functions for local inosphere modeling was studied at first. For this purpose, a local ionosphere model (LIM) was produced based on observation data from 16 Iranian permanent GPS stations and 5 IGS ones and B-spline basis functions. My assumptions in this modeling are as follows: first, the ionosphere is a thin shell that is located on 450 km above the Earth’s surface, second, the smoothed code station observations obtained by Bernese 5.0 software is considered as observation vector. Third, the weight matrix elements are proportional to the satellite elevation angle.  Forth, the differential code biases (DCBs) for all satellites which are obtained from IGS precise products, are considered as known parameters in the equations. And the last assumption was that a simple cosine mapping function was used to convert the slant total electron content (STEC) to the VTEC. As a result, the comparison between the LIM and the GIM showed that the B-spline basis functions were more efficient than the spherical harmonic ones for local ionosphere modeling. Following the first result, a new LIM, which is based on the B-spline basis functions, was produced by integration of permanent GPS station and Jason-2 satellite altimetry observations. The GPS and satellite altimetry observations were chosen from day 107 of year 2014, according to the latest maximum solar activity. The weight matrix of the GPS and satellite altimetry observations were determined based on the least-square varince component estimation (LS-VCE) method. The results showed that the local inosphere model derived from combination of the GPS and satelite altimetry observations were more accurate than the local inosphere model derived from the GPS observations only, this is due to the fact that the dual-frequency radar altimetry data are the main source of the ionospheric observations at sea, where there is no GPS permanent station, and can be used to improve the GIMs and LIMs. Finally As by-products, the DCB values for the permanent GPS recievers and the bias term between the GPS and satellite altimetry observations were determined.},  
Keywords = {B-Spline , DCB, GIM, LIM , LS-VCE, VTEC },
volume = {7},
Number = {3}, 
pages = {109-125}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-545-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-545-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Haji-Aghajany, S. and Amerian, Y.},  
title = {Comparing the Efficiency of Radiosonde and ERA-Interim Meteorological Data in Precise Point Positioning Tropospheric Delay Correction Using Three Dimensional Ray Tracing Method}, 
abstract ={The Earth’s atmosphere can be described by a model of layers. Although there are also horizontal gradients of the meteorological parameters, it is sufficient for a general model to divide the atmosphere into a vertical layer structure since the vertical gradients of the meteorological parameters are significantly larger than the horizontal ones. Furthermore due to the influence of the gravity the regional horizontal differences become smoothed out towards higher altitudes. The atmosphere can be divided into the troposphere, the stratosphere, the mesosphere, the thermosphere and the exosphere. The tropospheric path delay is one the errors in GNSS observations and reduces the accuracy of GNSS positioning. Accurate estimation of tropospheric path delay in GNSS signals is necessary for meteorological applications. The tropospheric delay is divided into the dry and wet parts. The dry tropospheric delay depends on the pressure variations between satellite and Earth’s surface and can be determined accurately using the Saastamoinen and Hopfield models. The wet delay can be determined by subtracting the dry delay from the total GPS derived delayIn this paper the effect of radiosonde and ERA-Interim data in increasing the accuracy of positioning is compared. European center for medium range weather forecasting (ECMWF) is currently publishing ERA-I, a global reanalysis of the data. This reanalysis provides values of several meteorological parameters on a global ∼75 km. The vertical stratification is described on 37 pressure levels. The piecewise-linear (PWL) is a simple and powerful 2D ray tracing technique which is fast and accurate in processing. The refined piecewise-linear (RPWL) technique is another 2D ray tracing technique. The 3D ray tracing technique based on Eikonal equation is the strongest and newest ray tracing method. These equations are solved in order to get the ray path and the optical path length. The Eikonal equation itself is the solution of the so-called Helmholtz equation with respect to electro-magnetic waves. In this method the ray paths are not limited to a certain azimuthally fixed vertical plane. Tropospheric corrections were calculated using both types of data using 3-D ray tracing method in Bandar Abbas and Birjand stations. The station coordinates were determined using two methods: 1-the tropospheric error was considered as unknown, 2-this error was not considered. Then tropospheric corrections obtained from ray tracing method was applied to the GPS observations and positioning was done. The Bernese GPS software version 5.2 has been used to process the GPS data. It performs ionosphere-free linear combination equation of dual frequency GPS observations from each site within the regional network. The ZTD was calculated using this software including raw data from the GPS observation network and the ZHD was calculated according to the Saastamoinen model. The results indicate the importance of tropospheric correction in precise positioning. In addition, these results indicate that the results obtained using radiosonde corrections in Bandar Abbas are more accurate than the results obtained using ERA-Interim data. Although the results of two types of data in Birjand station do not have much difference. This can be attributed to small variations of water vapor and other atmospheric parameters in Birjand station.},  
Keywords = {Tropospheric Delay, Precise Point Positioning, Meteorological Data, Radiosonde, Ray Tracing},
volume = {7},
Number = {3}, 
pages = {127-138}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-570-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-570-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Rezaei, M. and Arefi, H. and Rastiveis, H. and Sajadian, M.},  
title = {Building Extraction and Modeling Using LiDAR Point Clouds Imaging on Two-Dimensional Surface}, 
abstract ={Nowadays the three-dimensional presentation of real world features is very important and useful, and attracted researchers in various branches such as photogrammetry and geographic information systems and those interested in three-dimensional reconstruction of the building. Buildings are the most important part of a three dimensional model of a city, therefore extraction and modeling buildings of remote sensing data are important steps to build a urban digital model.   Although many efforts to reconstruct the three-dimensional building of LiDAR data have been made by researchers in recent years, but challenges still exist in this area, especially in urban areas. In previous studies, dense vegetation and tall trees in the vicinity of the buildings cause to difficulty in the building extraction process and reduction in the accuracy of the modeling results. The aim of this article is extraction and reconstruction of buildings by using LiDAR point clouds in urban areas with high vegetation. In this study, factors such as the LiDAR return pulse, the height of points and area of the region is used to separate the non-structural parts. Ground points in segment-based method by changing the size segment in each iteration by mean and standard deviation of the height of points in any segment extracted. The vegetation points are extracted and identified using LIDAR return pulse and a new method called "three-dimensional imaging of points on two-dimensional surfaces ". The projection process is done in the planes of XY, XZ and YZ. Using Illustration of points and changing the angle of view makes the point clouds be evaluated in different directions. Region expanding algorithm and length constraints imposed in different planes has an important role in the separation of dense vegetation. The modeling of building is done by using break lines and important vertices of the building roof in layers of roof height. Extraction of building edge points and height layers of roof is done separately in each building. This points are isolated by height analysis of the roof points. In the line approximations grouping the points in each height layer, line fitting and adjustment of line directions are factors that caused the break lines and points of the building roof to be correctly created. In roof modeling, the basic structure of the roof is modeled and then the parts on the roof are added to the model. The overall structure of the roof is made by roof vertexes and normal vector of generated planes. At the end, by calculating the point’s distances from roof plane, the roof parts are identified and the model of this components are added to the roof. The proposed method is evaluated on LiDAR point clouds in an area of the Stuttgart, Germany, with a density of 4 points per square meter. The accuracy of the proposed method is evaluated by visual interpretation and quantitative comparisons with information extracted by a human operator. The accuracy of proposed method is about 96 percent in extracting building points and modeling error at the corner of the building is approximately 44 cm. Overall, the results represent the success of the proposed method in extracting and modeling of buildings in areas with dense vegetation.},  
Keywords = {3D Model, Building Reconstruction, Lidar Data, 2D Plane, Segment-Based Approach},
volume = {7},
Number = {3}, 
pages = {139-150}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-361-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-361-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Neisani, Z. and Karimi, M. and Alesheikh, A. A.},  
title = {Development of an Urban Public Service Site Selection Tool Using Spatial Group Decision Making and Geo-Social Network (Case Study: Site Selection of Shopping Centers)}, 
abstract ={Site selection, as one of the key principles of urban planning, plays an important role and has a huge impact on the development of urban progression and citizen satisfaction. Urban site selection is a public process that will certainly create the most satisfaction with the views of citizens, experts, specialists and managers of the relevant fields. The purpose of the urban site selection is to determine the best possible site for an urban facility within a specific region. Site selection has a significant impact on the success or failure of a project. Especially in developing countries, public urban service centers failure is caused by the unsuitable site of the service. One of the main reasons for this challenge is related to the approach of decision making which is usually based on the experts’ knowledge. In this regard, the application of spatial group decision-making methods by means of the geo-social network can be effective in promoting urban development and site selection of public services. The purpose of this study is to develop an algorithm for site selection of public services centers via geo-social network platform in a case of shopping centers in the district six of city of Tehran. Also, the proposed approach attempted to integrate the opinions of different gropes of stakeholders including managers, experts and especially the citizens of the case study region. Initially, after examining the characteristics of urban land use in the study area and using environmental parameters, related factors were identified and evaluated. Then, the relevant spatial information was collected and make them ready for use in the system. Major criteria include: population, traffic congestion, slope, land value, accessibility. Accessibility criteria includs sub-criteria for a distance from business centers, distance from tourist centers, distance from main roads, distance from public transportation centers, and distance from parking lots. The software development environment is the Telegram social network with a different graphical user interface for collecting different opinions and showing the results. The decision-making process is carried out in tow steps. First, the weighting process is done with Analytical Hierarchy Process (AHP) by all the groups of stakeholders, and then we utilized the fuzzy majority for the integration of achievements in the first step. To assess this decision-making scenario, the statistical society was considered to be about 50 people (15 experts, 10 managers and 25 citizens of the region). The results showed that in group decisions, the dispersion of selected options and their compliance with the criteria is more appropriate, but in individual decision making, users tend to pay more attention to one or two criteria.  The results of the final decision of distribution were not fair. Experts' satisfaction from the result of the group decision making is 85% while in individual decision making it is decreased to 60%. Also, geo-social spatial group decision making is one of the most important tools available to urban planners and allows site selection for various public utility centers. The designed methodology demonstrated the efficiency of geo-social networks for solving the urban public services site selection problem.},  
Keywords = {Site Selection, Geosocial Network, Spatial Decision Making, Shopping Centers},
volume = {7},
Number = {3}, 
pages = {151-160}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-617-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-617-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Vahidnia, M. H. and Hosseinali, F. and Shafiee, M.},  
title = {Crowd-Source Mapping of Geographic Information Resources by Volunteer Users of Mobile Devices for the Purpose of Emergency Responses}, 
abstract ={After the first moments a crisis take place, quick emergency responses can be improved by updated spatial information and online map services of target places. Efficient use of GIS in the phase of response in crisis management requires having access to reliable data related to the crisis. Considering the critical situations at the initial moments of all disasters including earthquakes, floods, and accidents, as well as the great significance of geographic data in relief and providing the injured with appropriate care, the necessity of such data becomes apparent. The real time information acquired from crowdsourcing information can update the basic GIS server maps. Therefore, this study incorporates the capability of smart phone sensors, GPS, Web 2.0, VGI and Server-based technologies to design and develop a system for collecting target hazardous information from volunteers. Users can send information regarding the hazard location, its images, and other explanations to a central server through web technology and GPRS. This information and other crowdsourced information can be viewed by all users in real time through an updated map in the GIS Server. This online information can be used by relief groups so that they can hurry to the rescue of the injured with minimal loss of time. One of the most important contributions in designing this system is considering to the improvement of the positional accuracy of targets with respect to the position of the mobile device. Several approaches have been recommended for this purpose. The solutions include the use of online map services, the use of geocoding services, and the use of arithmetic methods based on the measurements of sensors embedded in a smart mobile phone. If a volunteered user for relief operations is convinced to determine the location using the various required methods and then send the results, the accuracy of the information can be verified with greater certainty.  The evaluation by a sample group of mobile users indicated that the system was easy to use and could be a substitute for telephone systems of reporting incidents. Fifty three and twenty four percent of the sample group of respondents agreed and completely agreed with this claim, respectively.  Based on the users’ views, if such a system is developed and used on a large scale, it will be widely employed to help crisis management and relief operations. Fifty-three percent of users agreed and thirty-five percent of them completely agreed with the claim that the system has a simple user interface and can be easily used. It was found that the initial location determined by the mobile positioning system could be improved through the use of online map services and/or by utilization of mobile sensors in the arithmetic method. The results showed that, from the positional accuracy perspective, the arithmetic method by the means of device embedded sensors would yield the best result. However, geocoding is more is more economical to save time. The system's evaluation also showed that this method would quickly compile target hazardous area information and provide guidance to relief groups.},  
Keywords = {Crowd-Source Mapping, Volunteer Geographic Information},
volume = {7},
Number = {3}, 
pages = {161-175}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-655-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-655-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Abzal, A. and Saadatseresht, M. and Babaei, A.},  
title = {Development of an Intelligent Optical Scanner with Capability of Feature Preserving Point Cloud Simplification}, 
abstract ={Three-dimensional measurement is one of the primary interests of various industries such as quality control, documentation of cultural artifacts and medical image processing. In recent years, contrary to the contact-based mechanical methods, active image-based methods using laser light [1] or white light [2] for recovering the surface of the object, have gained considerable attention because of their non-contact nature. Among these techniques, the phase-shifting digital fringe projection (DFP) is well-established due to its advantageous characteristics such as full resolution and high accuracy measurement [3]. Extremely dense point clouds that are obtained from structured light 3D scanners leads to many problems in further processing steps. The processing, modeling and visualizing of huge amount of points is a very hard problem for conventional computers [4]. Most of the simplification methods suggest maintaining details. As a result, the high frequency noise in data are also kept during simplification process which leads to decrease the signal to noise ratio (SNR) in simplified data. Although the goal of the simplification step is to decrease further complexities in processing of point cloud, this simplification process still encounters high computational complexity such as search for neighbor points in three-dimensional space, curve fitting and normal extraction of surfaces. All existing simplification methods are applied after point cloud generation in the post-processing step. So, there is a waste of costs in calculations on the processing of a part of data which is not required to be generated during the three-dimensional measurement. Therefore, an algorithm/method that smartly generates the minimum required points that perfectly reconstruct the object can efficiently decrease the post-processing cost. A hybrid scanner system is proposed in this paper that prevents the production of unnecessary points during measurement by DFP technique, using the geometric characteristics of the surface that are obtained from Photometric Stereo (PS) technique. The PS method generates surface normal from the object surface. The surface curvature can be obtained for each image pixel using PS normal. The curvature image is classified and assigned to the different level of densities. The density levels are defined in image of the stereo camera in scanner system. So by removing pixels in regular numbers the density of each level is constructed. Hence, the first level of density is the same as maximum resolution of camera. Next levels are equal to 50 percent, 33 percent, 25 percent, and 20 percent of all pixels which are respectively result of sampling every other pixel, sampling one pixel from every two pixels, from every three pixels, and from every four pixels. Though, the question arises here that what is the criteria of determining the curvature intervals which are assigned to density levels. The basis of determining the curvature intervals for point simplification is the distance between simplified points and surface computed from original dense point cloud. This distance is chosen by user.  In this paper it is equal to measurement system accuracy. So the unnecessary points are removed based on the curvature obtained via PS before calculation of 3D coordinates using FP technique. The surface normal obtained from PS has low high-frequency noise, so noisy data will not transfer to the simplified points. The simple hardware setup of PS technique provides an efficient tool for simplified measurement of DFP scanner. Also, the extraction and classification of geometric features of the object are performed in two-dimensional space with lower complexity in comparison with similar operation in three-dimensional space. The addition of PS method only adds the cost of a number of light sources to scanner system and also includes the addition of several images to the process of measurement. But on the other hand, it will reduce calculation time by 50% to 75% and will reduce the volume of data by 50% to 80% depending on the complexity of the geometry of the object. The reduction in density has been done with the assumption of maximum separation of simplified model from the main model with the distance of 0.01 mm and 0.02mm.   The principal idea of the proposed method is to measure surfaces with the minimal required points that preserves the geometry. Contrary to most of the simplification methods, the proposed method performs simplification while measures the surface, so no more post-processing step for simplification is required. At first, surface normal are calculated by PS technique. Then surface curvatures are computed for each pixel in camera image from normal vectors and classified. Each class represents a point density level. The surface slope is also considered to correct foreshortening effect that is caused by projective geometry. The output of previous step is a 2D simplification guidance map which is used to measure 3D objects surface with DFP technique. All measurement steps by proposed system are as follows: computing surface Normal and curvature assigning curvature ranges to point density levels 3D measurement by DFP based on simplification map},  
Keywords = { Intelligent Optical Scanner, Fringe Projection, Photometric Stereo, Point Cloud Simplification},
volume = {7},
Number = {3}, 
pages = {177-188}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-656-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-656-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {SafarzadehRamhormozi, R. and Karimi, M. and AlaeiMoghadam, S.},  
title = {Multi Objective Optimization of Urban Land Use Allocation Using Meta-heuristic Algorithms and Spatial Metrics}, 
abstract ={Today, urban land use planning and management is an essential need for many developing countries. So far, lots of multi objective optimization models for land use allocation have been developed in the world. These models will provide set of non-dominated solutions, all of which are simultaneously optimizing conflicting social, economic and ecological objective functions, making it more difficult for urban planners to choose the best solution. An issue that is often left unnoticed is the application of spatial pattern and structures of urban growth on models. Clearly solutions that correspond with urban spatial patterns are of higher priority for planners. Quantifying spatial patterns and structures of the city requires the use of spatial metrics. Thus, the main objective of this study is to support decision-making using multi objective Meta-heuristic algorithms for land use optimization and sorting the solutions with respect to the spatial pattern of urban growth. In the first step in this study, we applied the non-dominated sorting genetic algorithm ΙΙ (NSGA_II) and multi objective particle swarm optimization (MOPSO) to optimize land use allocation in the case study. The four objective functions of the proposed model were maximizing compatibility of adjacent land uses, maximizing physical land suitability, maximizing accessibility of each land use to main roads, and minimizing the cost of land use change. In the next step, the two mentioned optimization models were compared and solutions were sorted with respect to the spatial patterns of the city acquired through the use of spatial metrics. A case study of Tehran, the largest city in Iran, was conducted. The six land use classes of industrial, residential, green areas, wetlands, Barren, and other uses were acquired through satellite imagery during the period of 2000 and 2012. Three scenarios were predicted for urban growth spatial structure in 2018; the continuation of the existing trend from 2000 to 2018, fragmented growth, and aggregated growth of the patches. Finally, the convergence and repeatability of the two algorithms were in acceptable levels and the results clearly show the ability of the selected set of spatial metrics in quantifying and forecasting the structure of urban growth in the case study. In the resulted arrangements of land uses, the value of the objective functions were improved in comparison with the present arrangement. In conclusion planners will be able to better sort outputs of the proposed algorithms using spatial metrics, allowing for more reliable decisions regarding the spatial structure of the city. This achievement also indicates the ability of the proposed model in simulation of different scenarios in urban land use planning.},  
Keywords = {Spatial Multi-Objective Optimization, Urban Land-Use Planning, MOPSO, NSGA-II, Spatial Metrics, GIS},
volume = {7},
Number = {3}, 
pages = {189-212}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-607-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-607-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {KarimiFirozjaei, M. and KiavarzMogaddam, M. and Alavipanah, S. K. and Hamzeh, S.},  
title = {Normalizing Satellite Images-Derived Land Surface Temperature Relative to Environmental Parameters Based on the Soil and Vegetation Energy Balance Equations}, 
abstract ={Land surface temperature plays an important role in the physics of surface atmosphere interactions. It is at the same time a driver and a signature of the energy and mass exchanges over land. Land surface temperature is highly variable in both space and time mainly as a result of the heterogeneity of the meteorological forcing, land cover, soil water availability, surface radiative properties and topography. Therefore, satellite-derived land surface temperature is widely used in a variety of applications including evapotranspiration monitoring, climate change studies, soil moisture estimation, vegetation monitoring, urban climate studies and forest fire detection. The normalization of the surface temperature relative to environmental parameters is essential in scientific studies and management decisions of urban and non-urban areas. For the first time, a normalization method for topography-induced variations of instantaneous solar radiation and air temperature has been applied to satellite land surface temperature. While land surface temperature data are widely used over relatively flat areas, this new approach offers the opportunity for new applications over mountainous areas. As a significant perspective, such a normalization method could potentially be used in conjunction with land surface temperature-based evapotranspiration methods over agricultural and complex terrain, soil moisture disaggregation methods and forest fire prediction models, among others. In practice, when applyingthe normalized land surface temperature as into to energy balance models, the energy balance would be driven by the mean (instead of the spatially-variable) downward radiation within the study area as it is commonly done over flat areas. The aim of the current study is to utilize the physical model based on the soil and vegetation energy balance equations for normalizing the land surface temperature relative to environmental parameters. For this purpose, Landsat 7 satellite bands, AST08 Surface kinetic temperature, MODIS water vapor product, ASTER digital elevation model and meteorological and climatic data sets were used. In the current work, topographic factors, the radiation which reached the surface, albedo, environmental lapse rate and vegetation, were considered as environmental parameters. For calculating the surface temperature, the single channel algorithm was used. Moreover, for calculating the downward radiation to the surface, the albedo of the surface, lapse rate and vegetation; respectively, direct and diffuss radiation of the solar and neighboring surfaces, a combination of Landsat 8 reflective bands, the digital elevation model, and NDVI index, were used. Finally, by creating the energy balance equations for dry bare soil cover, wet bare soil, fully stressed vegetation and unstressed vegetation, the temperature of various coverages was extracted by exploiting Newton's method and by optimizing parameters of the model in both global and local optimizations and combining resultant temperatures; modeled and normalized surface temperature was obtained. The environmental parameters normalization model is calibrated in two main steps using Landsat land surface temperature observations. The first step minimizes the mean difference between observed and modeled land surface temperature. The second step adjusts environmental lapse rate, surface soil dryness index and vegetation water stress index by minimizing the RMSE between Landsat land surface temperature and model-derived land surface temperature. For evaluating the accuracy of the proposed model results, coefficient correlation indexes and RMSE were used between the modeled and observed surface temperature values as well as the variance of normalized surface temperature values. The results of this study indicate that in global optimization, the values ​​of the correlation coefficient, RMSE and variance for AST08 data were 0.89, 2.6 and 6.44, respectively for Landsat 7 data 0.93, 2.08 and 1.1 and in local optimization mode, the values ​​of these criteria for AST08 data were 0.962, 1.61 and 0.71 respectively and for the data of Landsat 7, 0.977, 1.2 and 0.13. The results of the study showed that, in both global and local optimization methods, the performance of Landsat 7 for normalizing the land surface temperature is higher than ASTER. Also, the use of local optimization method for global optimization to estimate the optimal values ​​of the missing parameters increased the accuracy of normalization results. The investigation of results of the relation between the surface temperature and considered environmental parameters in this study before and after the normalization indicate that the effect of environmental parameters on the surface temperature noticeably reduced after the normalization. Results of the current study imply the high efficiency of the proposed model for normalizing the surface temperature relative to environmental parameters.  Generally, the results of the research were an indicator of the efficiency of the proposed model for normalizing the surface temperature relative to environmental parameters.},  
Keywords = {Normalization, Surface Temperature, Environmental Parameters, Energy Balance, Soil, Vegetation},
volume = {7},
Number = {3}, 
pages = {213-232}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-661-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-661-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Rahimi, M. M. and Hakimpour, F.},  
title = {An Integrated Framework based on Cloud Computing for Map Matching Analysis of Floating Car Data}, 
abstract ={These days Floating Car Data (FCD) is one of the major data sources in Intelligent Transportation System (ITS) applications like route suggestion, traffic monitoring, traffic flow analysis and etc. Due to GPS limited accuracy and noises and road network errors, utilizing of FCD in ITS applications needs an efficient and accurate map matching framework. Map matching is a well-established problem which deals with mapping raw time stamped location traces to edge of road network graph. Along with high success rate, novel map matching applications faces several challenges including variable sampling frequency and processing speed of FCD big data. In this paper we have proposed a general, efficient, accurate and distributed map matching framework. The proposed framework can handle variable sampling frequency data accurately. Although this framework does not depend on additional data other than road network and GPS, achieved high success rate shows effectiveness of our system. We have used spatial proximity, heading difference, bearing difference and shortest path as our matching criteria. We also employed dynamic weights for each criteria to make our framework independent from local parameters. We have also employed confidence levels to improve our matching success rate. To answer low frequency data challenges, we have present an extra criteria based on A* shortest path method with dynamic weighting method. We have used HDOP for weighting shortest path criteria. When we are not confident enough about a point matching, we use shortest path criteria to improve success rate and by this method we keep our overhead low. For the evaluation we have studied New York City (NYC) OSM trajectories as the case study. We also used OSM NYC road network as the base map. The evaluation results indicate 95.2% MM success rate in high sampling mode (10s) along with 89.5% success rate in low sampling frequency (120s). We have compared our method with a known map matching method that In the case of low sampling frequency, our method has improved matching accuracy up to 9.7%. We have evaluated the effect of utilizing shortest path criteria in low frequency scenario. Our results show that using shortest path have improved our result up to 3.5%. One of the major challenges in using FCD is storage, managing, analysis and batch processing of this big data. To face this challenge in this framework we have used cloud computing technologies along with MapReduce paradigm based on Hadoop framework. The proposed cloud computing based framework can answer technical challenges for efficient and real-time storage, management, process and analyze of traffic big data. Our evaluation results indicate we have matched 7000 points/second on a cluster with 5 processing nodes. We have also processed 5 million records in 530 seconds using a cluster with 5 processing nodes. The main contributions are as follows: 1) we have proposed a general, distributed map matching framework using cloud computing technologies to answer to upstream ITS applications, 2) We have improved an efficient and accurate map matching algorithm which can handle different sampling frequencies using shortest path method and confidence level, 3) we have used dynamic method for weighting geometric, directional and shortest path constrains.},  
Keywords = {Floating Car Data, Map Matching, Cloud Computing},
volume = {7},
Number = {3}, 
pages = {233-251}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-642-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-642-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Shakeri, M. and SadeghiNiaraki, A. and Alimohammdi, A. and Alesheikh, A. A.},  
title = {Volunteered Spatial Data Infrastructure (VSDI) to Develop Collaboration System in Transportation}, 
abstract ={Spatial Data Infrastructure (SDI) has key role in management of many human activities by building a suitable information infrastructure for collaboration and cooperation among different data producer organizations and private sectors. SDI traditionally follows top-down approach that that only use data of organizations and private sectors, so does not consider user generated contents. In other side, web 2.0 platforms and GPS equipped devices provide user friendly tools for collaboration people in producing and sharing spatial data recently. This approach has enabled people even with no knowledge in spatial science to produce and share spatial data. These data that called volunteered geographic Information (VGI) are valuable data that could be integrated with other resources of SDI to complete and update SDI resources. VGI describes any type of content that has a geographic element which has been voluntarily collected. In this regard, user-centric SDI approach (the third generation of the SDI) that concentrate on user needs and preferences, while in the past SDI initiatives had concentrated mainly on technological issues such as data harmonization, standardized metadata models, standardized web services for data discovery, visualization and download, supports opportunities of VGI as viable means of updating and enriching SDI. However, some components of SDI should develop to consider valuable VGI resources. In this paper, Volunteered spatial data infrastructure (VSDI) model is proposed. In this model, VGI is used as other resources of spatial information in SDI. In addition, a system is developed based on VSDI concepts in road transportation projects. Road transportation projects and specially route selection project need to transform huge spatial data. In the route planning for road transportation, best route selection process is carried out with consideration of environmental, social and economic effects and as well as supplying technical transportation criteria to access sustainability. This process gets into trouble without suitable programming for public participation in route transportation planning and coordination between different related organizations and private sectors to the project. In addition, lack of sufficient information, lack of updated information or lack of same way to gathering required information, usually is one of the biggest obstacles to obtain the results based on real world. This VSDI based system could give decision makers update information of region to get better decisions by providing collocation environment. Based on the concepts of VSDI model, the system is designed by using web 2.0 technologies that enabled user to produce and share spatial data as well as to express opinions. To select best route in this system, fuzzy group AHP and VIKOR decision making methods are used with environmental, socio-economic, tourist and technical route characteristic’s criteria. The results of the research show that people like to collaborate in transportation planning and to share spatial data. So the use of VGI as another resource of SDI helps to make better and more effective decisions and to increase people satisfaction. The achieved results of implementation indicate that the system has good performance and selected route in the system corresponds to determined route by consult engineering company.},  
Keywords = {Volunteered Spatial Data Infrastructure (VSDI), Web 2.0, Transportation Planning, Route Selection.},
volume = {7},
Number = {4}, 
pages = {1-13}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-464-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-464-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {AhmadiBagh, M. and Khosravi, M. and HosseiniNaveAliabadi, A. and Varshosaz, M.},  
title = {Displacement Monitoring of Large Scale Structures with UAV Photogrammetry}, 
abstract ={Although close range photogrammetry has not been a common approach in the Civil Engineering field especially the displacement monitoring of large scale constructions, many project successfully used this approach and as a result this method have a high potential in this application. Nowadays, as a result of improving the computer vision and photogrammetry techniques implemented in many software and high resolution images captured by the off-the-shelf digital cameras, the use of progressive photogrammetric techniques instead of traditional displacement methods become more resendable. This paper aims to explain a novel method for monitoring of the large scale constructions based on visual inspection. This method can specify the displacement of the constructions based on photogrammetric and computer vision methods using drones. The evaluation of the proposed system is done on long wall in two epochs with a short time interval. Having known the zero displacement for this construction, the difference between the coordinates of some sample points obtained in first epoch and the second epoch, 1.89 mm, shows the accuracy of the proposed system in detecting displacement. Moreover, the accuracy of this photogrammetric method was investigated by developing a tool which manually provides a known displacement between two points in the second epoch. The displacements of these points were estimated using the proposed method and compared with the known displacement. The accuracy for this method, less than 2 mm, can confirm the capability of this method for such applications. },  
Keywords = {Displacement Monitoring, Photogrammetry, Computer Vision, UAV},
volume = {7},
Number = {4}, 
pages = {15-24}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-674-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-674-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Emami, H. and Jafari, A.},  
title = {Shadow Geothermal Energy Detection using Integrating of Temperature Anomalies and SEBAL Algorithm}, 
abstract ={With the increase in world population, industrialization and improvement in the standard of living, there has been a continuous increase in consumption of energy. In the recent years, a new resource of energy, gas-hydrates, is drawing worldwide attention. Detection and identification of suitable areas of shallow geothermal energy, using remote sensing data is one of the new methods in many applications. In areas of anomalously high heat flow, geothermal systems transfer heat to the Earth’s surface often forming surface expression such as hot spring, heated ground, and associated mineral deposits. Geothermal systems are increasingly important as sources of renewable energy, or as natural wonders of protected status attracting tourists, and their study is relevant to monitoring deeper magmatic processes. Thermal infrared (TIR) remote sensing provides a unique tool for mapping the surface expressions of geothermal activity as applied to the exploration for new geothermal power resources and long term monitoring studies. Airborne and space borne TIR data supports long-term monitoring of geothermal systems by providing a rapid and repeatable method of inventorying surface geothermal features. In addition, methods for relating the temperatures of surface geothermal phenomena to estimates of near-surface heat loss provide important inputs to the monitoring of geothermal activity and as applied to geothermal resource assessment and modeling. A geothermal resource can be simply defined as a reservoir inside the Earth from which heat can be extracted economically (cost wise less expensive than or comparable with other conventional sources of energy such as hydroelectric power or fossil fuels) and utilized for generating electric power or any other suitable industrial, agricultural or domestic application in the near future. Geothermal resources vary widely from one location to another, depending on the temperature and depth of the resource, the rock chemistry and the abundance of groundwater. Utilization of geothermal resources can broadly be classified into electric power generation and non-electric use. The type of the geothermal resource determines the method of its utilization. This research is based on applications of remote sensing as a decision support system that focused on the exploration of geothermal energy and environmental management. The aim of this study is to identify suitable areas for Shadow geothermal energy detection by integrating of land surface temperature (LST) anomalies and the energy flows of surface energy balance algorithms for land (SEBAL) algorithm using data LDCM data, , has been evaluated and analyzed in the North West of Iran. To this end, and because of at least the effect of solar radiation, two examined the scenes of LDCM data was used for dates October 13, 2016. Then, using two single-band algorithms (Radiative Transfer Equation (RTE) and SCJM&S) to calculate the LST and the LST anomaly maps of were identified. In addition, using the SEBAL Algorithm was calculating the amount of net radiation received by the Earth's surface (Rn), the amount of heat flow between the different layers of soil (G) and the amount of radiation absorbed by the solar surface (Rsolar). By assessing and combining this information layers with the LST anomaly maps the shadow geothermal prone areas were identified and determined.  The results showed that the areas between the cities of Marand and Tasuj as well as between Gator and Khoy cities prone shadow geothermal areas, the existence of large natural spa in the region, the possibility of geothermal resources increases and this is confirmed . Also, similar results were obtained in areas south of the city of Urmia and west of Oshnavieh. These obtained areas have the maximum distance that the location of energy consumption (in Urmia, Khoy, Marand, Tasuj, Sharafkhaneh and Oshnavieh) equal to 30 km, which is economically justified and it can provide a large part of the clean energy used in industry and cities and brings a healthy environment.},  
Keywords = {Geothermal Energy, Remote Sensing, Landsat 8, Land Surface Temperature, SEBAL},
volume = {7},
Number = {4}, 
pages = {25-44}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-648-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-648-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {JelokhaniNiaraki, M. R.},  
title = {Examining the Relationship between Criteria Ranges and Weights in Spatial Multicriteria Decision Analyses}, 
abstract ={Many real-world spatial decisions are multi-criteria by nature. A multi-criteria spatial decision analysis is a process in which one or more spatial alternatives are evaluated and selected based on a set of different criteria, by one or a group of individuals. Weighting the criteria is an important step of spatial multi-criteria decision-making. It represents the priority of that criterion relative to other criteria in decision making process. According to the range sensitivity principle, the weight of a criterion is the function of the range of changes in the values ​​of that criterion (the difference between the minimum and maximum values ​​for the criterion), in addition to its relative importance of the criterion. However, decision makers often ignore the range values when weighing the criteria in decision making processes. The main objective of this paper is to examine the research question “Do decision makers consider criteria ranges during the weighting process in a multi-criteria spatial decision-making process? Understanding how decision makers acquire and integrate decision-related information (i.e., criteria range values) helps to use an appropriate level of decision information in a multicriteria spatial decision analysis. In order to achieve the objectives of this research, the problem of locating public parking facilities in the district # 22 of Tehran was selected as the case study. The decision information including criteria values and ranges were presented to decision makers using decision table and map. The decision table represents the decision information in an alternative × attribute matrix. It consists of a set of values associated with each alternative-attribute pair. The rows of the matrix represent alternatives, the columns represent attributes, and the cells contain the measured values of the attributes associated with the alternatives. In addition to the alternative-attribute values, the table includes the range values of the attributes in the last row. The simultaneous map-table information aids facilitates understanding of the decision problem by enabling the decision makers to explore the basic relationships between the non-spatial attribute values of decision alternatives (criterion outcomes) and the spatial patterns of alternatives (decision space). The results show that decision makers in individual decision-making (without access to group decision) examined 55.5%, 26.8%, 25.5% and 14.5% of criteria ranges in four levels of decision-making information, respectively.  When it comes to the group decision-making mode (with access to group decision), they looked at 21.8%, 6.6%, 8.8%, and 7.9% of criteria ranges in the four levels. Overall, the results of ANOVA test show that this number decreases with increasing amount of decision making information. Therefore, it can be concluded that decision makers mainly consider the relative importance of the criteria, and in most cases ignore the ranges of changes in the criteria values, when faced with higher levels of decision-making information. The results of this study has implications for investigating behavioral theories in the spatial decision making context and practical implications for the development of multicriteria decision analysis. Explicitly, the findings provide a new perception on the use of decision support aids, and significant signs for designers to develop a suitable user-centered Web-based participatory decision analyses.},  
Keywords = {Spatial MCDA, Criteria Ranges, Weights},
volume = {7},
Number = {4}, 
pages = {45-55}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-676-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-676-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Bigdeli, B.},  
title = {A Multiple Fuzzy Classifier System for Fusion of Hyperspectral and LiDAR Data}, 
abstract ={Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors is effective at land cover classification. All these data have different characteristics, e.g., different spatial and spectral resolutions, different angle of view, and different abilities and disabilities. For many applications, the information provided by individual sensors is incomplete, inconsistent, or imprecise. Fusion of information from different sensors can produce a better understanding of the observed site, which is not possible with single sensor. Particularly, Light Detection And Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. On the other hand, hyperspectral imaging is a relatively new technique in remote sensing that acquires hundreds of images corresponding to different spectral channels. The rich spectral information of HS data increases the capability to distinguish different physical materials, leading to the potential of a more accurate image classification. As hyperspectral and LIDAR data provide complementary information (spectral reflectance, and vertical structure, respectively), a promising and challenging approach is to fuse these data in the information extraction procedure. This paper presents a multiple fuzzy classifier system (Multiple Classifier System or MCS) for fusions of hyperspectral and LiDAR data based on Decision Template (DT). After feature extraction on each data, the classification was performed by fuzzy K-Nearest Neighbor (KNN) on hyperspectral and LiDAR data separately. In a multiple fuzzy decision system, a set of decisions is first produced and then combined by a specific fusion method. The output of the fuzzy classifiers that provide the class belongingness of an input pattern to different classes is arranged in a matrix form defined as decision profile (DP) matrix. Then, a fuzzy decision fusion method (Decision Tempate) is utilized to fuse the results of fuzzy KNNs on hyperspectral and LiDAR data. In order to assess the fuzzy MCS proposed method, a crisp MCS based on (Support Vector Machine) SVM as crisp classifier and Naive Bayes (NB) as crisp classifier fusion method is applied on hyperspectral and LiDAR data. The experiments were executed on a hyperspectral image and a LiDAR derived Digital Surface Model (DSM); both with spatial resolution of 2.5 m. The dataset have captured over the University of Houston campus and the neighbouring urban area by the NSF-funded Centre for Airborne Laser Mapping (NCALM). Also hyperspectral image has 144 spectral bands in 380 nm to 1050 nm region. Training and testing samples were selected from different areas of the images. They are spatially disjointed. Fuzzy MCS on hyperspectral and LiDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data. Overall accuracies of fuzzy classifiers on LiDAR and hyperspectral data are %75 and %88 respectively. Fusion of these two fuzzy classifiers produced %96 as overall accuracy. Second scenario for joint use of hyperspectral and LiDAR data is fusion of these two data through a crisp decision fusion system. The results show that fuzzy classifier provided higher accuracies than crisp classification based on SVM for both data. In the presence of  mixed  coverage  pixels  in  remote  sensing  data, crisp  classifiers  may  produce  errors  while  fuzzy  classifiers  are not  affected  by  such  errors  and  in  principle  can  produce  a  classification  that  is  more  accurate  than  any  crisp  classifier.  Also, fusion of ensemble of fuzzy classifiers based on Decision Template method produced more accuracy than fusion of crisp SVMs based on Bayesian Theory.},  
Keywords = {Multiple Classifier System, Hyperspectral, LiDAR, Fuzzy Classification. Crisp Classification},
volume = {7},
Number = {4}, 
pages = {57-72}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-145-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-145-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Chamani, M. and AliAbbaspour, R. and Chehreghan, A. R.},  
title = {Matching of Polygon Objects Based on Geometric Measures in a Multi-scale Dataset}, 
abstract ={The task of identifying corresponding objects between different geospatial datasets is known as matching problem which has variety of applications, e.g., conflation, spatial data enrichment, updating, change detection, and quality assessment. Matching problems in vector data models can be divided into three categories of point, linear, and polygon problems according to the type of geometry are being used in matching process. Furthermore, the similarity measures utilized in order to calculate the degree of similarity between two objects can be classified into three groups of semantic, geometric, and spatial relation measures. Since there are few studies of polygon matching problems compared to linear objects and geometric measures are easily accessible, among these various kinds of matching problems, the purpose of this study is to propose an approach to identify corresponding polygon objects based on geometric properties. The proposed approach contains four stages of preprocessing, spatial similarity calculation, extraction of corresponding relations, and results analysis. The preprocessing stage consists of creating uniformity among data formats and coordinated systems, and also removing topological errors. In the similarity calculation stage, a probability based matching algorithm is presented in which the four similarity measures of distance, overlapped area, orientation, and shape are being used. Then, the six kinds of corresponding relations including 1:0, 0:1, 1:1, 1:N, N:1, and N:M relations are obtained as the result of similarity calculation stage. At the end, the results are analyzed through evaluation of the algorithm. Besides that, the impacts of each similarity measure, solitary and in combination with other measures, have been studied in the final precision of algorithm as the evaluation process. The implementation is carried out on the district 6 of Tehran city as the case study area by using two different datasets at the scale of 1:2000 and 1:25000. The evaluation of proposed method has been achieved according to three criterions of Precision, Recall, and F1-score. Also the manual matching of two datasets is needed to evaluate the proposed algorithm. The results show that the proposed algorithm by using all four similarity measures has reached the F1-score precision criteria of 99% which is quite high over the case study area. Furthermore, the influence of each similarity measure has been studies both solitary and in combination with other measures which shows that the precision is not necessarily increased by the increase in the number of similarity measures. As an illustration, the exclusive usage of overlapped area measure has far higher precision in compared with the utilization of two measures of distance and orientation. Consequently, in order to decrease the cost and time of processing, it is better to use the least number of similarity measures which positively affect the algorithm precision. Also the precision of proposed method was compared to one of the latest work of polygon matching problems using geometric measures.  The results demonstrate that the precision of polygon matching problem has been improved compared to the previous work.   },  
Keywords = {Matching, Polygon Objects, Geometry of Objects, Multi-scale Dataset},
volume = {7},
Number = {4}, 
pages = {73-87}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-646-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-646-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Abolhoseini, S. and Alesheikh, A. A.},  
title = {Developing a Mobile Recommender System and Tour Planner for Individual Tourists}, 
abstract ={Nowadays, tourists are planning trips by their own using the available services on the web. Travelling individually does not have the pleasure of group tours, so some tourists decide to find similar friends on their destination to share the joy of tours and trips. Also, they may not be familiar with the routes available in the city. In this paper, a mobile application is designed, developed, implemented and evaluated to recommend similar tourists to each other and generate optimal route based for each individual. This system is integrating mobile GIS, recommender systems and artificial intelligence tour planning algorithms into a single application. Being on a mobile platform is necessary, especially for navigation and tour planning systems. User may have to carry his cellphone in order to follow the suggested tour. The recommender system is a demographic filtering recommender to find the similar tourists. It considers age, nationality, education and interest to find the proper matches. After suggesting similar tourists to each other, one of the versions of Ant Colony Optimization namely Ant System is used to plan the optimal tours for each individual. This problem can be defined as a new version of Multiple Travelling Salesman Problem with mutual nodes to visit simultaneously. Different tourists decide to share a tour that stay in different places. This system should find a proper sequence of sightsees to minimize the length of the tour and also it should maximize the number of mutual streets in a tour to let tourists travel together during the tour. The objective function is to minimize the sum of the distances each tourist traversed and to maximize the number of mutual streets with a proper POI sequence. Maximizing number of mutual streets was considered because tourists want to travel as a group in a shared trip. Implementation of the system took place in Shiraz, Iran. Shiraz is the fifth-most-populous city of Iran and the capital of Fars Province with more than 4000 years of history that made it one of the key tourism sites in Iran. Numbers of questionnaires were distributed among different people to evaluate the people recommendation system. In addition, tour planning algorithm was evaluated using a well-known evolutionary algorithm (i.e. Genetic Algorithm). Mean, standard deviation and the convergence graphs of the two mentioned algorithms were compared. Results indicated accurate performance of the recommender system and high accuracy and precision of the route planning algorithm. Recommender System had a mean difference of 0.2 with the questionnaire results, which indicated its good functionality. Ant System reached the minimum value of the objective function (34.70) with a better standard deviation (0.61) compared to Genetic Algorithm. However, Genetic Algorithm performed better in mean value of the tests (34.32) which is a measure of the precision. Convergence graph of the Genetic Algorithm showed a fast convergence with lower objective values in the beginning. Ant System convergence graph showed a smooth convergence toward the optimal solution with an initial population with higher objective values. This indicated the proper functionality of the Ant System and the possibility of improving the results with generating better initial population.  },  
Keywords = {Tourism, Mobile GIS, Recommender System, Tour Planning, Ant System Algorithm.},
volume = {7},
Number = {4}, 
pages = {89-101}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-668-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-668-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Soufi, M. and Behzadi, S. and Aghamohammadi, H.},  
title = {Developing a Baseline Approach for Modeling Land use Change}, 
abstract ={In recent decades, we have witnessed a rapid and increasing growth in urban population and urbanization. One of the most notable effects of rapid urbanization in countries such as Iran is the emergence of a phenomenon known as land scarcity, which in turn leads to a struggle for land ownership in urban areas. The most noticeable political measure taken by urban masses in many cities is the illegal seizure of land in areas outside city limits. Such measures have contributed to the unplanned urban development and have further hindered future urban land-use planning efforts. Rapid growth of technology and increased complexity of daily activities have encouraged people to seek awareness and knowledge about the latest technologies in an attempt to adapt themselves to the world around them. At the same time, limitations have prompted individuals to use natural resources responsibly, take actions to prevent environmental damage, and put in an increasingly great deal of effort to improve workforce health and conditions. Today, the use of agent-based technologies in solving problems associated with the approaches and methods applied in urban land-use planning efforts has considerably increased. In the present study, Tehran’s urban growth was investigated within a five-year period. The model’s agents included parcels of land, physical limitations, suitability, and protected areas. In each time step, these agents were able to change their status from undeveloped to developed. This study was aimed at developing a dynamic agent-based model to model urban growth as the major factor influencing land-use changes. An agent’s decision to change its status depended on the specified eligibility criteria and the existing limitations and constrains. In addition, each agent’s relevant features were also assessed. Finally, an overview of the agent-based modeling (ABM) approach and its integration into a geographic information system (GIS) were presented. The findings showed that if the land suitability were estimated 50 and 100, the number of developed parcels of land would be 4192 and 4424, respectively. This results indicated that the behaviors of Tehran residents followed the initial estimated land suitability (100).},  
Keywords = {Based Modelling, Land-Use, Urban Planning, Geographic Information System (GIS)},
volume = {7},
Number = {4}, 
pages = {103-118}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-654-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-654-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Darabi, H. and Pirnia, A. and Choubin, B. and Rozbeh, S.},  
title = {Urban Growth Modeling and Prediction using Logistic Regression and Markov Chain in Sari}, 
abstract ={Land use changes is an ecological processes in global and local status and it will be major problem in the twenty one century and even scientists believe more impact of land use changes than climate change. One of the methods used in planning to control land use changes, is its modelling. This research aims to predict land use changes carried out and with emphasis on physical development in Sari city using logistic regression and Markov chain. To analyze land use changes in the central area of the Sari city, TM sensor (Landsat 5) for 1987, 2001 and 2011 were used. For this purpose, the images taken from the USGS web, the necessary pre-processing (including radiometric and atmospheric correction) was performed in ENVI 5.1 software. Then, using supervised classification method and maximum likelihood algorithm land use maps were extracted in the study area. At this stage to predict of Land use, maps prepared imported to IDRISI software. Transition potential modeling was conducted using logistic regression in thee IDRISI software. The 6 variables were used (including two variable as a dynamic and four variables as a static variable) and 3 sub-models calibrated over time (1987- 2001, 2001- 2011 and 1987-2011). In order to prediction of land use in 2011 year, the calibration period of 1987-2011 using Markov chain model. And hard prediction was used. For accuracy assessment of LCM the KIA parameters was used. Finally from the 1987-2011 period in order to predict changes in land use, land use maps in 2025 and 2039 were used. Results showed that during 1987-2011, important changes occurred, including increasing of (4.49%) in residential area and decreasing in agricultural area by 13.4%. Also the results of transition potential modeling using logistic showed high accuracy in all scenarios (0.72 to 0.92). Kappa coefficient in the land use modeling for 2011 with calibration periods 1987-2011 and reference map in 2011 was higher than in other scenarios. The modeling results for the years 2025 and 2039 showed that physical development Sari in West, South, North and the East directions 8.02, 6.47, 6.37 and 4.41 % are respectively.},  
Keywords = {Modeling, Urban Growth, Landsat Satellite, Logistic Regression and Markov Chain},
volume = {7},
Number = {4}, 
pages = {119-131}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-599-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-599-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Karimipour, F. and Tayebi, M. and Amozande, K.},  
title = {Characterization of Social Land use in Urban Environments Based on the Semantic Dimension of Location Based Social Networks’ Data}, 
abstract ={Recognizing the urban environments and understanding the citizens’ motion behavior is an important research field in the area of spatial data analysis. The location-based social networks record and gather update, rich, and enormous data that users share them honestly through their spatial, temporal and semantic behavior. Undoubtedly the physical structure of an urban area as well as its land use impress the spatial behavior of its citizens and this impression propagates to the data of location-based social networks. Because of that, nowadays, researchers use the users’ data in location-based social networks in order to recognize urban environments. In this research, we attempted to cluster urban environments based on social land uses by using the location-based social networks’ spatial and semantic data. In this regard, in the first step, the spatial data of users are clustered by employing a clustering algorithm that is based on a competitive neural network (SOM). To cluster the spatial data of users, we first should calculate the optimal number of clusters. In this regard, Elbow chart was used as DB index. Then, the urban environment is partitioned into several regions by drawing the Voronoi diagram on the cluster centers and the data which users have been recorded in each region are identified. The number of data available in each region was computed for semantic categories separately, then the vector of each region was normalized. Similarly, these operations were repeated for all data in whole urban environment and the. The initial idea is usage of the abundance of each category of semantic data; however, this criterion cannot determine the land use of a region properly; because it is possible that users share more information about, for example, creation places than residential ones. Finally after extracting the percentage of the different groups of semantic data and by considering the weight of each group, a semantic dimension that is the representative of the region’s social land use was assigned to each region by taking advantage of a clustering algorithm based on the semantic dimension of users’ data. To evaluate the proposed method, the number of data in each category was calculated for every 15 minutes of a day to verify the validity of data that users share about their activities in the foursquare social network. To more accurate study, the working days and weekend days were studied separately; i.e. for each category, we formed a vector with 192 members. The chart of temporal variations of data numbers during a day (24 hours) was plotted for clusters identified from proposed method too. Then, the correlation among these charts was used as the evaluation index of the proposed method. This research and the performed evaluation show that the big data of social networks are not only low cost and updated but also shared by citizens honestly and have suitable validity. Also, the urban regions with common or similar social land uses have spatial continuity. The results of the research show the high potential of the location-based social networks to recognize urban environments.  },  
Keywords = {Location-based Social Networks, SOM Clustering, the Land use of Urban Environments, DB Index},
volume = {7},
Number = {4}, 
pages = {133-145}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-663-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-663-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {BazrgarBajestani, A. R. and AkhoondzadehHanzaei, M.},  
title = {ESTARFM Model for Fusion of LST Products of MODIS and ASTER Sensors to Retrieve the High Resolution Land Surface Temperature Map}, 
abstract ={   Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, urban heat islands, fire detection and earthquake thermal precursors. There is a shortage of daily high spatial land surface temperature data for using in high spatial and temporal resolution environmental process monitoring. Due to the technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result one sensor doesn’t provide both high spatial resolution and high temporal resolution. The 16-day revisit cycle of ASTER leads to a disadvantage in studying the global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors’ ability to quantify biophysical processes in heterogeneous landscapes. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. Since ASTER and MODIS sensors are onboard a platform (Terra or Aqua), therefore, this study has used an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm to blend ASTER and MODIS LST product. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from ASTER can be blended for applications that require high resolution in both time and space. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “daily” LST product at ASTER spatial resolution. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. The results show that the accuracy of ESTARFM algorithm is better than the accuracy of the STARFM algorithm in the studied area—with an average difference of 1.77 Kelvin from the real observation data. The STARFM algorithm couldn’t preserve the spatial details in the predicted virtual image as well as two other algorithms. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations with a high correlation coefficient (r) of 0.87 between synthetic imageries and the actual observations.  },  
Keywords = {LST, Fusion, Thermal Product of ASTER Sensor, Multi Source Data},
volume = {7},
Number = {4}, 
pages = {147-161}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-690-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-690-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Mohammadzadeh, M. R. and Arefi, H. and Alidoost, F.},  
title = {Comprehensive Evaluation of Modeling and Surface Simplification Methods for 3D Building Reconstruction from Dense Point Cloud}, 
abstract ={3D building reconstruction is a mathematic model and representation of 3D surfaces for building details in urban areas. There are many methods for 3D modeling such as Image Based Rendering (IBR), Image Based Modeling (IBM) and Range Based Modeling (RBM). These methods use generated 3D point cloud from different techniques sources such aerial laser scanners and photogrammetry multi view imageries. In this paper, 3D model generation methods based on triangulation algorithms such as Poisson, ball-pivoting and volumetric triangulation using Marching Cubes (MC) are evaluated using a raw dense point cloud. Also two mesh simplification methods called clustering decimation and quadric edge collapse are used to improve the quality of triangulated models with decrease the surface and vertex numbers. A geometric metric called Hausdorff distance is used for comparison of each model with a reference. The results show that the accuracy of generated 3D model based on volumetric triangulation method using Marching Cubes (MC) is better than other methods. Also, quadric edge collapse method can simplified 3D models better than clustering decimation method.},  
Keywords = {Surface Triangulation, Mesh Simplification, 3D Modeling, Hausdorff Distance},
volume = {7},
Number = {4}, 
pages = {163-175}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-662-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-662-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Ghods, S. and Shojaeddini, S. V. and Maghsoudi, Y.},  
title = {Determining the Optimal Boundaries of Alpha-entropy Classification Zone for Dual Circular Polarimetric Data Using the Concept of Maximum Similarity}, 
abstract ={Nowadays, SAR imaging is a well-developed remote sensing technique for providing high spatial resolution images of the Earth’s surface which provides a vast amount of information for environmental monitoring. Fully polarimetric (FP) SAR systems alternately transmit two orthogonal polarizations and receive the response of the scatters to each of them by two antennas with orthogonal polarizations. Transmitting two interleaved electromagnetic waves requires doubling the pulse repetition frequency which implies immediately that the image swath must be only half of the width of a single-polarized or dual-polarized SAR. In order to achieve a better swath width, and coincidentally reduce average power requirements and simplify transmitting hardware, compact polarimetric (CP) systems have been proposed with the promise of being able to maintain many capabilities of fully polarimetric systems (Souyris et al., 2005). One of the most important CP configurations is dual circular polarimetric (DCP) mode.        In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is H-α decomposition (Cloude and Pottier, 1998) that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. The space of scattering entropy (H) and mean alpha angle (α) namely H-α plane is used to classify the polarimetric image into 8 canonical PSMs.         In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. (Guo et al., 2012) proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for CP data and subsequently (Zhang et al., 2014) proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’s H-α space, however Zhang’s method is not well accurate and there are still overlaps between different PSMs. The results of Zhang’s method for H- α boundaries is highly dependent on the choice of data. For example, in one data it might exist a special class of plants that are dominant in the image and in another one another class might be dominant. So, the maximum distribution densities of these two images are different from each other. Furthermore, the specifications of different sensors are different. For example, the base noise of each sensor is different and entropy is dependent on this parameter. So, for each specific sensor its own optimum boundaries should be found. According to the fact that fully polarimetric data contains maximum polarimetric information, the efforts of the researchers in this field is to achieve the nearest information from CP data to FP data. Therefore, in this research we have found the H-α boundaries of DCP data which maximize the total class agreement of classification results of the DCP and FP data for RADARSAT-2 sensor. Two images over San Francisco and Vancouver acquired by Radarsat-2 at C-band in quad polarization mode, with the image size being 1151×1776 and 1766×1558 respectively have been used for this study. In order to evaluate the ability of the proposed H-α zones in comparison with Zhang’s zones, Each experimental image is classified into eight PSMs. Confusion matrices have been achieved and the resultant mean agreements  have been calculated. It has been shown that the proposed boundaries have increased the mean agreements of the results by 3%.        In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is Cloude–Pottier H-α decomposition that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. Entropy and α-angle are two important parameters for the interpretation of fully polarimetric data which are extracted from this method. They indicate the randomness of the polarisation of the back scattered waves and the scattering mechanisms of the targets respectively. For fully polarimetric data an H-α classification space has been presented. This H-α classification space is devided by H and α borders and cllassifies 8 feasible PSM regions without the need for training data.         In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. In 2012, Guo proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for DCP data and extract approximate borders. Subsequently, in 2014 Zhang proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’s H-α space, however Zhang’s method is not well accurate and there are still overlaps between different PSMs. Both Zhang’s and Guo’s methods are not based on an optimization method. Therefore, they do not present optimum H-α borders for classification of DCP data. Furthermore, each sensor has its own specifications. One of which is the system noise floor which affects entropy borders for classification. Thus, it is important to find optimum H-α boundaries for each sensor separately.         In this paper we have proposed a novel approach for finding optimum H/α classification borders for DCP data. The optimum borders have been found in such a way to maximize the agreement of the H-α classification results of DCP data with the H-α classification results of FP data. ‘Mean class agreement’ is introduced and the borders which maximize this parameter have been found. The results of classification using the proposed borders have been compared with the rival method and the superiority of the proposed method has been revealed.},  
Keywords = {Polarimetry, Compact Polarimetry, Classification, Entropy, Alpha},
volume = {7},
Number = {4}, 
pages = {177-190}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-528-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-528-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Pordel, F. and Ebrahimi, A. and Azizi, Z.},  
title = {Modeling of Green Canopy Cover of Marjan Rangelands, Boroujen During Growing Season Using Spectral Indices of OLI Sensor}, 
abstract ={Vegetation is considered as an important measuring indicator in rangelands, which play a significant role in ecological processes. In this regard and due to the lack of models in estimating green vegetation canopy, this research aimed to evaluate temporal changes of canopy cover of vegetation in Marjan rangelands of Broujen. As the completion of the past investigated studies in other areas, it is tried to present the growth stages and time effect on destroyed green canopy cover through a model which has an appropriate estimation power throughout the growing season and for a time such that the education sample do not exist. To this end, the canopy of green vegetation was measured in 19 sampling points over a length of 10 km transects. In each of the points, there are about five quadrats as one quadrat in center and four quadrats at the four directions around the central quadrat (a total of 95 quadrats in a period). Sampling points were placed with distance of 400-1000 meter apart from each other. Measurements were repeated during 4 periods of field operations (in total 380 quadrats). Thus regarding to the index type (Atmospheric Resistant Vegetation Index and the moderating effect of soil line) 12 vegetation indices due to the use of multi-temporal images and the effect of soil reflectance were calculated in this arid region. In the next stage, the model of estimating green plant canopy was prepared using the regression equations between obtained canopy cover from five sampling periods and derived vegetation indices of Landsat 8 image data. Thus this model was also generated to prepare the vegetation cover map to other 4 periods which have not education data. Finally canopy cover map of vegetation was prepared at the 8 periods during the growth season (April to mid-September). Using image differencing technique, the changes of the vegetation coverage of area was estimated. Validation of cover estimating model represents no significant difference between estimated cover and land cover data. It is concluded that achieving to the growth model which can be used for all seasons are possible by using Landsat 8 images. The results showed that the indicators of ARVI, SARVI and EVI (with coefficients of determination about 8.0) have the best correlation relationship with canopy cover. So not only there is a significant relationship between vegetation indices and vegetation in arid areas, also it is possible to prepare only a unique data model for estimating all different time of growing seasons. According to the results of this research, the vegetation is in its peak on May among the eight growth times in the Mrjan rangelands and the canopy cover has heterogeneous distribution. In terms of coverage changes during the growing season, most of area is categorized in small variations class. In the period from April to mid-May we are witnessed for increased class in the region, but after this period, reduction in area were the dominant phenomena, as well as amplification of increased class area from 3 to 19 September is related to the emergence of new autumn species in the highlands region.},  
Keywords = {Spectral Indices, Rangeland Growing Season, Change of Green Canopy Cover, OLI Sensor},
volume = {7},
Number = {4}, 
pages = {191-203}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-635-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-635-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Tarighat, F. and Mohammadzade, A. and Janalipour, M. and Sahebi, M. R.},  
title = {Automatic Selection of Color Constancy Algorithms for Enhancement of Object Detecting in Shadow Area of Remote Sensing Images by Fuzzy Rule-based Reasoning}, 
abstract ={Most of the times identifying the terrains in some points of some images which are influenced by the others is difficult. So, some algorithms must be developed in such points. the auto selection color constancy algorithms have been indicated as a highly applicable algorithm to improve identifying of dark non metric laboratorial images. This paper is aimed to investigate and assess capability of this algorithms to reconstruct of images of remote sensing. By using a fuzzy logic, these algorithms help to choose an appropriate color selection algorithm of Gray-Edge, Gray-World or White-Patch. These algorithms are considered because of precision movement of light in addition to significantly illustration of color in images. Also, the study area has been divided into 50 equal sections in order to assessing the presented method and then the application of GE, GW and WP has been assessed in each section by two experts. Since the study area has the different shadow positions and objects, the existent information of sections is not integrated and the assessing of the result would be reliable. It is shown in most of the times the presented method has better results in clarifying of shadow terrains and could better clarify the edge of the terrains. },  
Keywords = {Clarifying, Fuzzy Logic, Frame-based System, Image Processing},
volume = {7},
Number = {4}, 
pages = {205-216}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-683-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-683-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}

@article{ 
author = {Neshat, A. R. and Dadras, M. and Safarpour, S.},  
title = {A GIS-based Comparative Study of Statistical Methods for Timeworn Urban Texture Susceptibility Mapping in Bandar Abbas city, Iran}, 
abstract ={During recent years various studies have been conducted concerning the trend of urban structure changes, and the decay process in the district of urban neighborhoods. In the present study, the Multi-Criteria Decision Making (MCDM) model and some other statistical models have been used in order to provide a susceptibility map of decayed area. In addition, different criteria have been implemented to identify and analyze the decayed area. According to a review of background literature, the present study takes into account four criteria: i.e. ecological and environmental, economic, social and structural based on specified sub-criteria for the purpose of providing and analyzing the susceptibility map of decayed area. The quality and quantity of the collected information are of pivotal importance in the studies of urban decayed area. The wider the range of parameters related to research the collected spatial and attribute data cover, and the more accurate these data are, the more precise and high-quality the results obtained from the analysis will be naturally. By doing so, the study will provide a better representation of existing real conditions. The aim of the present study is to assess the susceptibility of decayed area in the city of Bandar Abbas, located in southern Iran. In order to achieve this purpose, five methods (i.e. AHP, Frequency Ratio, Statistical Index (Wi), Weighting Factor (Wf), and Logistic Regression) were applied together with their combination with the Geographic Information System (GIS) techniques. During the past years, lack of special attention to urban district, particularly old area has resulted in their degradation and inefficiency. Today, identification and determination of their susceptibility is very essential and important for the urban planning of Bandar Abbas for the present and future. On the other hand, the decay of urban district has led to economic recession, creation of social problems, threats to the security of citizens, and its impacts on/transferring to other adjacent district. In order to mitigate the impacts of this phenomenon, scientific assessment of urban areas exposed to the process of decayed area, or those affected by decay is vitally important. To achieve this, mapping was performed on areas prone to decayed areas; and the effective parameters on the occurrence of decay in the urban district were analyzed using the six mentioned different methods. In order to confirm the results obtained from these six methods and their concordance with the susceptibility map of decayed area, field observations and data collection was performed at 1300 points of the city with dilapidated and abandoned buildings. Afterward, the methods with the most accurate results were determined and selected. Validation shows that the Wf method presents results with the highest degree of precision, compared to the other five methods (i.e. AHP, Frequency Ratio, Statistical Index (Wi), and Logistic Regression).},  
Keywords = {Urban Decayed Area, Susceptibility Map, Multi-Criteria Decision Making, Statistical Model, GIS},
volume = {7},
Number = {4}, 
pages = {217-232}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-679-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-679-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2018}  
}