@article{ author = {EbrahimianGhajari, Y. and BarariSiavoshkolaei, M.}, title = {Runoff Production Potential Zoning Using Fuzzy GIS-MCDA Models (Case Study: Tajan River Basin)}, abstract ={Because in Iran, flood phenomenon has the highest financial losses and death tolls among the natural disasters, thus reducing the damage caused by it has been considered for a long time and it is regarded essential. The most urgent action to deal with this natural disaster is to make preparations and take measures to reduce its harmful effects. One of the basic measures in this regard is the recognition of flood-stricken areas and the zoning of these areas in terms of the risk of flooding or runoff production. As a result, it is possible to decide on the use of land and various applications, such as agriculture, urban, industrial, etc., and minimize flood damages. In this study, first the factors affecting flood formation such as geology, precipitation, drainage density, distance from the waterway, concentration time, slope, aspect, soil material and land use were identified. Then, using the GIS capabilities and the fuzzy hierarchical analytical method, the zoning map of flood gas potential of the Tajan basin was prepared in five classes of very low, low, moderate, high and very high floodwater potential. Accordingly, 0.51% of the basin areas have very high runoff potential, 5.48% of the regions are with low runoff potential, 29.09% are with medium run runoff potential, 52.4% are with high runoff potential and 12.52% are with very high runoff production potential. Also, the range of Sari city is in the range from medium to high runoff potential, and 87 of the 441 villages in the studied area have very high runoff potential and are in the danger zone. In general, the situation of the roads in the basin is not suitable for the runoff production potential, and the main parts of the roads in the catchment area, including the main roads such as the parts of the Sari-Semnan axis, have very high run runoff production potential. Flood Damage alleviation both by structural or Non-Structural measures firstly requires accurate identification of study area in terms of flood risk or runoff production potential. This is more important due to financial limitations and the lack of an integrated management perspective. The presence of a zoning map of runoff production potential in Tajan basin is more important as it is located in border of Mazandaran and Semnan provinces and since some of the flood-prone areas are located in Shirin Rood tributaries at Chashm district of Semnan province and its floods impose substantial damage to downstream areas in Mazandaran province. Hence, this zonation map and its results, i.e. identifying runoff prone areas, assist us to define, complete and develop watershed project at   upstream of flood-prone areas with high runoff potential with holistic view as well as proper prioritization in management decisions. Also through management of the financial and human resources, roads prone to flooding can be secured and it helps to prevent the establishment of industries and population centers and the implementation of civil constructions and projects in the high risk areas. At the same time   in credit and budget allocation for flood and education areas with high runoff generation potential are prioritized. It is also possible to consider the zoning map of runoff production potential in the selection of plowing and furrow methods, tillage and flood protection and flood control measures.}, Keywords = {Runoff Production Potential, Tajan River Basin, Geospatila Information Systems, Fuzzy Set Theory, Change Extent Analysis}, volume = {9}, Number = {1}, pages = {1-14}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-809-en.html}, eprint = {http://jgst.issgeac.ir/article-1-809-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Afary, A. R. and Varshosaz, M. and SaadatSherest, M. and Mojaradi, B.}, title = {GPS Jamming Detection in UAV Navigation Using Visual Odometry and HOD Trajectory Descriptor}, abstract ={Auto-navigating of unmanned aerial vehicles (UAV) in the outdoor environment is performed by using the Global positioning system (GPS) receiver. The power of the GPS signal on the earth surface is very low. This can affect the performance of GPS receivers in the environments contaminated with the other source of radio frequency interference (RFI). GPS jamming and spoofing are the most serious and intentional RFI attacks. Due to the jamming attacks, the positioning accuracy of UAV will be degraded. This positioning error can be reached to tens of kilometers off the true location as was reported in some research articles. The detection of GPS jamming is the first step to mitigate this attack. Most of jamming detection methods are based on GPS signals processing. If a jamming detection system just relies on GPS signal processing to detect and confront RFI threats, it will be capable of failure in the use of the more advanced hardware and sophisticated methods by invaders intentional. The camera is a common sensor almost in all of UAVs which is a passive sensor and resistant to the jamming signals. Here, the use of image-based navigation methods for GPS jamming detection will be helpful due to the insensitivity of camera sensors to jamming signals. GPS jamming attacks can be detected in UAV navigation, independently of the signal processing methods, using a comparison of two flight trajectories assigned for a UAV, determined from visual navigation and GPS positioning data. For this purpose, the trajectory descriptor of the Normalized Distance of the Consecutive Points (NDCP) and trajectory descriptor of the Consecutive Directions Angles (CDA) is used. These descriptors are independent of the coordinate system of the trajectory but are not independent of the number of trajectory points. As a result of the jamming attacks, the GPS receiver may not be able to receive the signal and the positioning cannot be performed. Therefore, two trajectories of UAV from GPS and visual navigation may have a different number of points. So, the NDCP and CDA cannot be used for these trajectories in all the time. The trajectory descriptor of Histogram of the Oriented Displacements (HOD) is independent of the number of trajectory points, but it is not independent of the coordinate system of the trajectory. In this paper, a method is developed to allow the use of the HOD trajectory descriptor to detect the occurrence of the GPS jamming attacks. For this purpose, first, the coordinate system of the UAV trajectory from GPS data is transformed to the coordinate system of the UAV trajectory from visual navigation. Here, a sliding window-based approach is used for determining of the jamming location. The performance of the HOD trajectory descriptor in detecting jamming attacks versus the NDCP and CDA trajectory descriptors were compared. The results show that the HOD trajectory descriptor has a significant advantage in detecting the jamming attacks concerning NDCP and CDA trajectory descriptors, especially in the positioning errors of more than ten meters due to jamming attacks, which can be more reliable. This descriptor can be used to detect the occurrence of the jamming attacks. The ability of the HOD trajectory descriptor is significant in detecting positioning errors greater than five meters, compared to the other two trajectory descriptors.}, Keywords = {UAV, GPS, Jamming Detection, Visual Odometry, Trajectory Descriptor, HOD}, volume = {9}, Number = {1}, pages = {15-30}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-810-en.html}, eprint = {http://jgst.issgeac.ir/article-1-810-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {OmraniKhiabanian, Sh. and Alesheikh, A. A. and Abbasi, O. R.}, title = {Developing a Recommendation Framework for Tourist by Mining Geo-tag Photos (Case Study Tehran District 6)}, abstract ={With the increasing popularity of sharing media on social networks and facilitating access to location technologies, such as Global Positioning System (GPS), people are more interested to share their own photos and videos. The world wide web users are no longer the sole consumer but they are producers of information also, hence a wealth of information are available on web 2.0 applications. The shared media usually contain geo-tagged locations, time stamp, hashtags, and comments. As such, mining social networks can yield extensive knowledge about human dynamics and mobility behaviors within urban context So web users are no longer just users but also producers of information This wealth of information can be leveraged for location-based services. If the locations visited by users are collected and sorted according to the timestamps, the sequence that the user has visited can be determined; exploring of which can be used in tourism planning. Recently, there is an increasing tendency to adopt the information from these geo-tagged photos for learning to recommend tourist locations. For a tourist, before traveling to an unfamiliar city, the most important preparation is planning the trip. without any prior knowledge, tourist must either rely on travel books, personal travel blogs or combination of online resources and services. It is difficult and time consuming and painstaking to find out the locations worth to visit and figure out the order in which they are to be visited. Hence, the purpose of the present study is to provide a framework for recommending locations and travel sequences to tourists by using geo-tagged photos in social networks. Most existing methods for tourist recommendation do not consider context constraints, or at best, address a few dimensions of contexts. The present work aims to develop a context-aware recommender system that recommends interesting locations and the travel sequence. The proposed method is designed such that it can use the collective wisdom of people from collection of geo-tagged photos in order to provide a set of tourism locations and interesting trip sequences that matches the user's current context given a city that is unfamiliar to that user.  first Due to the low accuracy of positioning with GPS embedded in mobile phones to find a unique pair of geographic coordinates for a tourist place the geo-tagged photos were clustered. For this reason OPTICS clustering method exploit to group geo-tagged photos by their locations. It then uses the combined method, to annotate all of the clusters that are created in the previous step with semantics. Then, we create a profile for clusters by using historical context (time stamps and weather). After that, we generated a travel sequences database and rated the sequences in the database according to their context. Finally In order to evaluate the performance of the proposed method, Panoromia site dataset of one region in Tehran was used and Experimental results showed that 64.5% of the results obtained by our proposed strategy are identical with the user preferences, which illustrate rationality of the recommendation from analyzing the geo-tagged photos.}, Keywords = {Location Based Social Network, Recommender System,Travel Planning,Context Aware}, volume = {9}, Number = {1}, pages = {31-42}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-799-en.html}, eprint = {http://jgst.issgeac.ir/article-1-799-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Mirzaei, M. and Arefi, H.}, title = {Low Cost UAV-based Remote Sensing for Autonomous Wildlife Monitoring}, abstract ={In recent years, developments in unmanned aerial vehicles, lightweight on-board computers, and low-cost thermal imaging sensors offer a new opportunity for wildlife monitoring. In contrast with traditional methods now surveying endangered species to obtain population and location has become more cost-effective and least time-consuming. In this paper, a low-cost UAV-based remote sensing platform is introduced. It is a small, low-cost, flexible remote sensing platform, which was accomplished object detection, classification, and measures the accurate GPS coordinates of animals in wild or agriculture areas. The paper describes both hardware and software architecture of a UAV such as thermal image sensor, an RGB camera, on-board computer, object detection, and recognition algorithms. While flying the onboard computer run a developed program to achieve real-time detection and path planning. The proposed system proved to be capable of detecting 83% of animals with a spatial accuracy of 2.17 meters.}, Keywords = {UAV, Drone, Remote Sensing, Wildlife Survey, Thermal Infrared and Visible Imagery, Real-Time Image Processing}, volume = {9}, Number = {1}, pages = {43-55}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-791-en.html}, eprint = {http://jgst.issgeac.ir/article-1-791-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Asmar, E. and SadeghiNiaraki, A. and AbdehKolahchi, A. and Rezaei, M.}, title = {Scrutiny of TRMM Satellite Precipitation Data Efficiency for Evaluation of Rainfall Damages on Gilan’s Province Rice Farming}, abstract ={Rice is considered as one of the most strategic plants in Iran. The production of rice has been confronted with new challenges since the year of 2000, so the necessity of supporting policies implemented by all relevant authorities has been revealed more and more. Average rainfall records in Gilan province, a rice-favored tropical region, often show a range of 800 ml – 2000 ml with the latter number knowns as the desirable quantity for rice farming. Generally, rice farming needs humidity of 70-80 per cent and Gilan province is well-known for its tropical climate that formulates the necessities for successful productive rice farming industry in this specified district. Long tedious hours of work roughly reach out to 1050-1400 hours for each hectare of rice farm so that all three stages of the rice farming process could be completed respectively. This long hours of work commands stretched hours of pay for the staff that may leave the business overdrawn in the long run. With population overgrowth and lack of unlimited resources, a need for research and development in terms of optimized rice productivity, is inevitably on the rise. Each advanced plan that guarantees higher product yield with better quality boosts the country one step closer to economical and political independence. A comprehensive knowledge over rainfall locations and its seasonal changes enhances the farming productivity and plays key role in the agricultural risk management and its crops insurance. Considering rice dominance in Iran’s farming, use of old fashioned methods in evaluation of the damages imposed by different hazards; such as heavy rainfalls, seems defectively inefficient due to the fact that the surveys are conducted in a very short period of time with a great deal of cost.  Recent developments in the field of Remote Sensing (RS) have popularized the technique; as it offers unique vast insight, high levels of data transition speed, and availability of field-specialized software/hardware. In this research, TRMM_3B42 data which is known as TRMM data (version 07), is utilized for measuring precipitation. In an attempt to evaluate the efficiency of TRMM in reporting the damages occurred to Gilan’s province rice farms, Pearson correlation coefficient, was compared among processed data and the gathered observational results separately across the larger states where rice farms cover the area of 15 hectares and less than that, also few villages in Rasht district so that the results indicate the promising use of method in Gilan’s province ultimately since Pearson correlation coefficient between calculations and observations equals to 0.945, so it is meaningful in the statistical level of 1%, although in the occasion of smaller villages its efficiency was graded as poor.}, Keywords = {Rainfall Damages, Rice, TRMM, Crops Insurance, Gilan’s Province}, volume = {9}, Number = {1}, pages = {57-64}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-769-en.html}, eprint = {http://jgst.issgeac.ir/article-1-769-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Zamani, Z. and Alimohammadi, A. and Farnaghi, M.}, title = {Exploring the Relationships between Spatial and Demographic Parameters and Urban Water Consumption in Esfahan Using Association Rule Mining}, abstract ={Water is considered as a vital limited resource worldwide. Due to its geographical location, Iran suffers from a semi-arid and arid climate. In recent years, due to excessive use of water resources and long-lasting drought, the country has faced severe water scarcity. In order to deal with such a problem, the country needs to have proper water resource management strategy and practice. One of the most critical issues in this regard is related to the monitoring and management of urban water consumption. Exploring the pattern of urban water consumption and the relationships between geographic and demographic parameters and water usage is an essential requirement for effective management of water resources. In this study, association rule mining has been used to analyze the data of public water consumption in the city of Esfahan. Association rules mining have been used to discover the connections between geographic and demographic parameters including the number of family members, the number of apartment units, residential building types, areal coverage of the house and green spaces, distance from river and population centers, spatial location, distance from main roads, population density and percentage of young population with water consumption patterns. A version of the apriori algorithm called Liu, with suitable computational characteristics to process large amounts of data, has been used for association rule mining. This algorithm, using classification methods, provides the possibility to extract a broader range of association rules from the data. The output of the algorithm is a set of rules that can be subjected to study further using statistical methods. Each of the extracted rules that satisfy minimum support equal 30 and minimum confidence equal 60 reflects the relationship of each spatial and demographic parameters with the consumption water. Then, the obtained rules have been evaluated, and water consumption hot spots were extracted. The evaluation procedure consists of two parts. The first part examines the spatial pattern of household water consumption distribution. The second part investigates the distribution pattern of household water consumption by using Moran’s spatial autocorrelation analysis and identifies hot spots of consumption. Getis Ord's Gi test and hot spot analysis have been used to determine hot spots. Results show that some of the geographic and demographic parameters are associated with reducing consumption and some with increasing consumption. Distance from the main road, areal coverage of the residential and green spaces have a direct relationship with household water consumption. On the other hand, the number of residential units, population density, distance from the river, X and Y values, and percentage of the young population are inversely related to household water consumption. Also, if the distance from the city center increases, household water consumption decreases. Moreover, by moving to the north and east, the consumption of each unit reduces, and the southern areas of the city by minimum distance from the river have the highest water consumption and specified as hot spots. In these neighborhoods, building types are villa and have a lower population density with larger areas of green spaces and yards.  }, Keywords = {Association Rules Mining, Data Mining, Water Consumption, Geographic Information System}, volume = {9}, Number = {1}, pages = {65-81}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-735-en.html}, eprint = {http://jgst.issgeac.ir/article-1-735-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Zarezadeh, F. and Hosseininaveh, A. and Habibi, Z.}, title = {Infant Head Circumference Measurement Using Deep Learning Techniques}, abstract ={Infant's head circumference measurement and and its growth monitoring plays a crucial role in diagnosis the diseases which cause a deformation in the infant's head. Due to the fact that the contact measurement, which is performed using a tape measure and a caliper, has problems such as transmitting disease, infecting, not comfortable and disruption relaxing the baby, going to non-contact measurements is unavoidable. The purpose of this study is to provide a non-contact image based method for measuring the infant's head circumference. In this study, an algorithm was developed that calculates the infant's head circumference using an image taken above the infant's head and the scale index next to the head. The first step in calculating the head circumference is detecting and segmenting the baby's head in the image. In this regard, two the state of the art deep learning algorithms, MaskR-CNN and CRF-RNN, were compared in this study for accurately segmenting the infant's head. Subsequently, the head circumference pixels were detected by a fusion of the Canny edge detection and morphology algorithms. In the next step, the ground sample distance at suitable level was calculated using the scale tag in the image. Finally, the head circumference was calculated using the ground sample distance value and the number of pixels forming the head circumference. The evaluations show that the MaskR_CNN method with a total accuracy of 98.8% is a more appropriate method than the CRF-RNN method for detection and segmentation of the head in the image. Also by comparing the results of the proposed algorithm with the actual values obtained by strip meter on 10 images, it was found that the error of the proposed method is about 1 to 3%.}, Keywords = {Non-contact Measurement, Deep Learning, Convolutional Neural Network, Object Detection, Segmentation}, volume = {9}, Number = {1}, pages = {83-101}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-822-en.html}, eprint = {http://jgst.issgeac.ir/article-1-822-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Yousefi, H. and Noorollahi, Y. and Mosavi, S. M. M. and Alimohammadi, A. and Saedi, M.}, title = {GIS-Based Small-Scale Hydropower Resources Assessment in Kurdistan - Iran}, abstract ={The industry development and the life standards increment have caused the human feels more easement, but it also has caused an increase of energy consumption, environmental pollution and the large volume of greenhouse gases emission. Those changes make the researchers to find out the alternative energy sources such as renewable energies. The most prominent and the oldest source of clean and renewable energies is hydropower. There is no international consensus on the definition of small scale hydropower. In Canada 'small scale' can refer to upper limit capacities of between 20 and 25 MW, in the United States 'small scale' can mean 30 MW, however, a value of up to 10 MW total capacity is becoming generally accepted. Small scale hydropower can be further subdivided into mini hydro (usually defined as <500kW) and micro hydro (<100kW). No matter how you define it one thing remains the same, small scale hydropower is one of the most environmentally benign forms of energy generation available to us today.   Small scale hydropower systems capture the energy in flowing water and convert it to usable energy. Although the potential for small hydro-electric systems depends on the availability of suitable water flow, where the resource exists it can provide cheap clean reliable electricity. A well designed small hydropower system can blend with its surroundings and have minimal negative environmental impacts. Moreover, small hydropower has a huge, as yet untapped potential in most areas of the world and can make a significant contribution to future energy needs. It depends largely on already proven and developed technology, yet there is considerable scope for development and optimization of this technology. In other, more rugged regions of the country, it is possible to develop relatively higher heads without elaborate or expensive civil engineering works so that relatively smaller flows are required to develop the desired power. In these cases, it may be possible to construct a relatively simple diversion structure and obtain the highest drop by diverting flows at the top of a waterfall or steeply falling watercourse. Small hydropower plants are a precursor for economic growth and social development. They guarantee a local, stable power supply. Small hydropower plants are often the only way to create environmentally-friendly power for electric lighting, for preparing and cooling food and for stimulating economic growth in remote regions. In developing countries, they are a good substitute for diesel-powered generators. Even in industrialized nations, small hydropower plants are in demand, as they provide a useful contribution on the whole and effectively support energy change. In this study, has been tried to evaluate the existence and assessment the resources of this clean source of energy in Kurdistan- the northwestern province of Iran and the calculate the extraction amount of energy from this resource of energy due to the high potentials of the province. In the evaluated small-scale hydroelectric power in this study, after full identification river and its catchment areas in terms of flow and head and after extraction of relevant maps, by using ArcGIS software, and after apply appropriate environmental, geographical and technical- economic criteria, the hydropower potential of the province from its rivers was estimated.  The results show that Kurdistan province capable of producing around 500 megawatts of renewable power from that small-scale hydropower that it has. As a result, 3455 small-scale hydropower plant with the average capacity of 160 kW could be located in the suggested suitable locations in the Kurdistan province. These proposed areas are selected by considering the appropriate environmental, geographical and technical- economic criteria.}, Keywords = {Geospatial Information System (GIS), Small-Scale Hydropower, Site Selection Criteria, Kurdistan Province}, volume = {9}, Number = {1}, pages = {103-118}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-628-en.html}, eprint = {http://jgst.issgeac.ir/article-1-628-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Miranzadeh, S. and Shamsoddini, A. and Mousivand, A.}, title = {SPOT-5 Spectral and Textural Data Fusion for Forest Mean Age and Height Estimation}, abstract ={Precise estimation of the forest structural parameters supports decision makers for sustainable management of the forests. Moreover, timber volume estimation and consequently the economic value of a forest can be derived based on the structural parameter quantization. Mean age and height of the trees are two important parameters for estimating the productivity of the plantations. This research aims to estimate mean height and age of a Pinus radiata plantation using SPOT-5 textural and spectral data derived from multi-spectral and panchromatic images, respectively. The study site for this research consisted of a 5000 ha Pinus radiata plantation from 35◦ 23/ 35// S to 35◦ 29/ 58// latitude, and 147◦ 58/ 48// E to 148◦ 04/ 02// E longitude, near the town of Batlow in the Hume Forestry Region, NSW Australia. Tree age classes ranged from 10-20 years, 21-30 years and more than 30 years. A total of 63 plots with radii varied from 7 m to 20 m to ensure there were a minimum of 15 trees per plot in variable stocking classes, were randomly surveyed in June and July 2008 by the New South Wales Department of Industry and Investment (IINSW) and Forests New South Wales (FNSW). The effects of forest boundaries, road edges and the other irrelevant features on the estimations were eliminated through buffering prior to locating the plot centres. Tree heights and DBH were measured for 978 trees. Tree heights were measured twice using Vertex hypsometer to increase the accuracy of the measurement. After calculating mean height of the plots, a regression equation was derived based on the relationship between tree age and mean height of the plots. Using this equation and available age of each segment, the mean height was calculated for 278 segments of this plantation. Spectral data includes band reflectance, vegetation indices, and principal component analysis were derived using multispectral image for each segment. Gray level co-occurrence matrix was calculated in four different angles and window sizes to extract the textural data from panchromatic image. Prior to modeling, random forest feature selection method was applied on the spectral and textural data, individually and together to determine the most important features for estimating mean height and age at segment level. Multiple-linear regression (MLR) was applied to model mean height and age using textural and spectral data. Also, spectral and textural attributes were fused at feature level using two approaches. In the first approach, the spectral and textural attributes were used together as inputs for MLR. In the second approach, ratio of spectral and textural attributes were calculated for feeding MLR. The results indicated that there is not significant difference between the models derived from spectral attributes of multispectral data and those derived from textural attributes of panchromatic data. Moreover, it was shown that the models derived from the ratio of the spectral and textural data with age estimation error of 17% and height estimation error of 13% performed better than the other models.   }, Keywords = {Optical Images, Spectral Data, Textural Data, Data Fusion, Structural Parameters}, volume = {9}, Number = {1}, pages = {119-130}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-750-en.html}, eprint = {http://jgst.issgeac.ir/article-1-750-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Dabiri, S. S. and Alesheikh, A. A. and Atazadeh, B. and Baeimi, A.}, title = {Developing a BIM-based Spatial Ontology for Semantic Querying of 3D Property Information}, abstract ={With the growing dominance of complex and multi-level urban structures, current cadastral systems, which are often developed based on 2D representations, are not capable of providing unambiguous spatial information about urban properties. Therefore, the concept of 3D cadastre is proposed to support 3D digital representation of land and properties and facilitate the communication of legal ownership rights associated with them. On the other hand, Building Information Modeling (BIM) provides a common data environment for integrated management of 3D building lifecycle data in the construction industry. Harnessing BIM for 3D cadastre will provide the ability to capture and manage the ownership and legal status of properties in modern cities, providing an efficient approach to access legal information about urban properties. However, querying the meaning of property information inside BIM models has not been addressed. There are studies which investigated the adoption of the semantic web techniques for a better understanding of concepts of the building lifecycle in BIM; however, these investigations do not provide a semantic approach to query and retrieve property information encoded in the BIM environment. Therefore, this study aims to adopt a linked data methodology to develop a BIM-based spatial ontology for managing 3D property information. In addition, SPARQL query language will be used to retrieve 3D property information. The appropriate entities for modelling properties the in the open BIM data model were deciphered and the spatial ontology was developed in accordance with the BIM open data model structure. To assess the feasibility of the developed spatial ontology, BIM models of multi-story buildings with different levels of complexity and detail were implemented. These models were transformed into the RDF (Resource Description Framework) structure, and 3D semantic queries related to 3D property information have been done in semantic BIM models. These queries retrieve boundaries and spatial arrangements of properties. The query results were displayed using a WebGL viewer, and retrieval time of each query was measured. The findings of this study indicate semantic querying of 3D property information is faster than spatial queries relying on the geometry of 3D property objects.}, Keywords = {BIM, Ontology, Linked Data, SPARQL, Cadastre}, volume = {9}, Number = {1}, pages = {131-144}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-835-en.html}, eprint = {http://jgst.issgeac.ir/article-1-835-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Ameri, A. and DadrassJavan, F. and Zarrinpanjeh, N.}, title = {Automatic Pavement Crack Detection Based on Aerial Imagery}, abstract ={Road health information is an important indicator for assessing the status of the road in management systems. Identifying the abandonment of surfaces is an important process in maintaining roads and traffic safety, which is traditionally conducted on the basis of field surveys. Today, remote sensing methods, especially photogrammetric imaging, are presented. In this article, based on by UAVs images, the road surface cracks are extracted. The proposed method consists of six stages of acquisition of images, separating the road from the background, identifying and eliminating disturbing objects, preprocessing, combining morphological filters with complication features, applying SVM classifications, generation of crack map .The results of the proposed method show the success rate of 98% in the extraction of the cracks.}, Keywords = {Crack, Detection, Aerial, Imagery, SVM}, volume = {9}, Number = {1}, pages = {145-160}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-795-en.html}, eprint = {http://jgst.issgeac.ir/article-1-795-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Mehrabi, H.}, title = {Retrieving Three Dimensional Displacements of InSAR Through Regularized Least Squares Variance Component Estimation}, abstract ={Measuring the 3D displacement fields provide essential information regarding the Earth crust interaction and the mantle rheology. The interferometric synthetic aperture radar (InSAR) has an appropriate capability in revealing the displacements of the Earth’s crust. Although, it measures the real 3D displacements in the line of sight (LOS) direction. The 3D displacement vectors can be retrieved through multiple InSAR measurements acquired from at least three independent imaging geometries in a theoretical manner. However, this is a physically ill-posed inverse problem and consequently, the retrieving process of the components in 3D displacements become sensitive to observation errors, especially in the northern component due to the near-polar orbiting of SAR missions. Combining different datasets regarding this issue requires proper treatment of the weight of observations, which otherwise will have a negative effect on both the precision and accuracy of the estimated 3D displacement field. In retrieving the 3D displacement fields through InSAR technique, we deal with two major issues, integration of inhomogeneous precision of observations and instability of the estimation problem. These facts constitute the motivations to address the Tikhonov regularization (TR) and least squares variance component estimation (LS-VCE). In this article, to overcome these drawbacks, the regularized least squares variance component estimation (RLS-VCE) is proposed for retrieving the 3D displacement vectors. Usually, the number of InSAR observations in relation to the three unknowns of 3D displacements for each pixel is not enough to apply VCE. Therefore, observations of some neighborhood cells are taken into account to increase the redundancy of stochastic model. In this context, a moving frame including a window of 3 × 3 pixels is considered to increase the number of observations and consequently, the degree of freedom of stochastic model. To assess the efficiency of the proposed method, the RADAR dataset of the Envisat and ALOS missions for the 17 June 2007 eruption of Kilauea volcano on Hawaiian island are applied. To validate the results of the proposed method, co-event displacement vectors of 19 GNSS stations around the Kilauea volcano are used. Furthermore, the 3D displacements of GNSS stations are applied for detrending the displacements of InSAR from systematic or random disturbing effects (e.g. orbit errors, curvature and topography of the Earth, atmosphere, etc.) through fitting a two variates linear or quadratic polynomial. Comparing the co-event retrieved 3D displacement vectors through RLS-VCE method and GNSS measurements indicates that the componential RMSE of northern displacements decreases drastically to 2.2 cm from 11.7 cm (for range displacements and primary weights). This is approximately equivalent to 80% improvement in the accuracy of estimating the northern component of displacement. The overall RMSE of retrieving 3D displacement vectors decrease from 7.8 cm to 2.6 cm, which is equal to 66% improvement. Achieving to this overall accuracy and for northern component is of major interest for all disciplines of geoscience dealing with 3D surface deformation analysis. Results indicate that retrieving the 3D displacement vectors through applying the RLS-VCE method has a meaningful improvement on the precision and accuracy of the results, the northern-southern component in special.}, Keywords = {Retrieving 3D Displacement Vectors, Least Squares Variance Component Estimation, Regularization, Synthetic Aperture RADAR Interferometery }, volume = {9}, Number = {1}, pages = {161-171}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-785-en.html}, eprint = {http://jgst.issgeac.ir/article-1-785-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {GholamiBidkhani, N. and Mobasheri, M. R.}, title = {Development of an Index-based Regression Model for Soil Moisture Estimation Using MODIS Imageries by Considering Soil Texture Effects}, abstract ={Soil moisture content (SMC) is one of the most significant variables in drought assessment and climate change. Near-real time and accurate monitoring of this quantity by means of remote sensing (RS) is a useful strategy at regional scales. So far, various methods for the SMC estimation using a RS data have been developed. The use of spectral information based on a small range of electromagnetic spectrum in the form of a single index cannot be a suitable way to estimate the moisture content in the optical and thermal RS. Single use of each index has its own limitations. These limitations can include the effect of atmospheric conditions, solar illumination geometry, topographic conditions and soil characteristics. Therefore, the use of all potential of the electromagnetic spectrum (visible to short-wave infrared bands) in the form of regression combinations of spectral indices might be useful in improving the accuracy of SMC estimation. In this study, at first, the correlation of 20 indices commonly used in soil moisture estimation studies with in situ soil moisture measurements were evaluated. In the next step, based on the correlation results, the indices gradually were added into the soil moisture linear regression and the accuracy of each stage was evaluated. The best SMC estimation model was included the linear regression made of LST, VSDI, NDWI and SASI indices. This ended up with and improvement accuracy (RMSE = 0.048). Soil texture is one of the most important factors in the estimation of SMC by means of RS data especially in optical and thermal regions of spectrum. On the other hand, due to the fact that the soils have different levels of porosities, it seems that the SMC modelling should be based on soil texture. Therefore, the SMC model was evaluated for three dataset, medium textured, moderately coarse texture and coarse texture soils. The results showed that the medium texture soils have a profound relationship with in situ measured compared to coarse texture soils (RRMSE=29%…, RRMSE=0.032). On the other words, the SMC model is not generalizable for all soil types. The results showed an inverse relationship between the accuracy in the SMC estimation and the soil particle size. In other words, the accuracy of the SMC model decreased by the increase in soil particle size. In the case of medium texture soils, better response to the SMC estimation have been seen in optical bands. Coarse texture soils such as sandy soil, because of porosity, water penetrates rapidly and freely inside the soil due to the force of gravity and show a lower water content capacity. On the contrary, medium texture soils have the ability to retain more water in their textures and the length of capillary rise in these soils is greater than those of coarse texture soils. Thus, moisture variations in this type of soils have a greater effect on the soil spectral responses compared to the coarse texture soils where the results of the SMC modelling for loamy medium texture soils approves this.}, Keywords = {Soil Moisture Content, Soil Texture, Soil Spectral Index, Remote Sensing, MODIS}, volume = {9}, Number = {1}, pages = {173-187}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-837-en.html}, eprint = {http://jgst.issgeac.ir/article-1-837-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Sedaghat, A. and Mohammadi, N.}, title = {Evaluation of Similarity Measures for Template Matching}, abstract ={Image matching is a critical process in various photogrammetry, computer vision and remote sensing applications such as image registration, 3D model reconstruction, change detection, image fusion, pattern recognition, autonomous navigation, and digital elevation model (DEM) generation and orientation. The primary goal of the image matching process is to establish the correspondence between two images of the same scene (i.e., the reference and input images). Image matching methods are generally classified as feature-based matching and template matching. Feature-based methods extract image features (points, lines, regions) and attempt to establish the correspondence between these features. Template matching methods, also known as area-based methods, are generally defined as the process of finding a template in an image, based on a similarity measure such as cross-correlation and mutual information. Identical image windows of predefined size are applied for the computation of correspondence. Similarity measures play an essential role in the quality of template matching in photogrammetry, remote sensing, and computer vision. Various similarity measures have been proposed in the literature. Each similarity measure has its strengths and weaknesses. In this paper, the capability of some well-known similarity measures for matching of various close range and satellite images with diverse geometric and radiometric differences are evaluated. Also, to increase the template matching stability against geometric and radiometric variations, a novel weighting approach for computing of similarity measures has been introduced. The proposed approach is based on three weight factor that are computed using gradient and Gaussian functions. By applying this weighting approach for cross correlation similarity measure, a novel measure named Weighted Cross-Correlation (WCC) has been presented. Ten algorithms, including SSD (Sum of Squared Differences), LSSSD (Locally Scaled Sum of Squared Differences), NSSD (Normalized Sum of Squared Differences), JF (Jeffrey Divergence), Tanimoto, ISD (Incremental Sign Distance), IRV (Intensity-Ratio Variance), CC (Cross-Correlation), MI (Mutual Information) and WCC are considered for evaluation. To evaluate the capability of various similarity measures, a number of template-matching experiments were applied. Several synthetic and real images for different geometric and radiometric variations including, scale, rotation, viewpoint, blur, and illumination changes are used as data set. The similarity measures are evaluated using three evaluation criteria, including success rate, positional accuracy, and computation time. The experimental results indicate that the proposed WCC method outperforms the other similarity measures for all images and all types of transformations. Based on the evaluation results, the WCC method can be applied to the reliable template matching for a variety of photogrammetric and remote sensing applications. Generally, after the WCC, better results, on average, were obtained by the NSSD, LSSSD, and CC measures in most cases. For illumination variations, MI and ISD methods provide the best results. The fastest method is the IRV and the slowest method is MI. Evaluation of the performance of the various similarity measures for other applications such as dense matching process is suggested as future work.}, Keywords = {Template Matching, Similarity Measures, Weighting, Gaussian Function, Cross-Correlation}, volume = {9}, Number = {1}, pages = {189-206}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-774-en.html}, eprint = {http://jgst.issgeac.ir/article-1-774-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Kiani, A. and Ebadi, H. and FarnoodAhmadi, F.}, title = {Automatic Interpretation of UltraCam Imagery by Combination of Support Vector Machine and Knowledge-based Systems}, abstract ={With the development of digital sensors, an increasing number of high-resolution images are available. Interpretation of these images is not possible manually, which necessitates seeking for practical, fast and automatic solutions to solve the environmental and location-based management problems. The land cover classification using high-resolution imagery is a difficult process because of the complexity of the landscapes, and the spatial and spectral resolution of images. The predecessor studies of land cover classification were done using statistical methods such as maximum likelihood classification. However, the newer studies apply the artificial intelligence techniques such as artificial neural networks and support vector machines as a substitute for classification applications. A major problem with using these models is that the user cannot easily understand the final rules. In this paper, a hybrid algorithm is proposed in order to obtain the needed data by the knowledge-based system from the input data set. The proposed algorithm is designed to get better training data and improvement of the learning system in semi-urban areas by classes covered by different material and colors. Classification of the remote sensing images refers to separation of the similar spectral sets and division of the units with the same spectral behavior. In remote sensing, due to the large size of data, the processing procedure is costly. On the other hand, to achieve better results, it has been recommended to use various features in the training procedure, which will consequently incrementally increase the volume of processing. Accordingly, the use of object-based process can increase velocity and homogeneity of the final interpreted image by reducing the computational base units. In the field of feature generation, a hybrid feature including both region-based (by kernels) and object-based (by segments) strategy, has been employed in this study. In order to produce the training data, needless of determining that by the user, utilize the capacity of integration of the multi-source data by KBS based system. For this purpose, the ontology concept that applying by the knowledge-based rules was used. Then to improve the obtained training samples and compensate its defects in the expression of the target class properties, the correction step is done. In the other words, the automatic Knowledge-based method was performed to apply the ontological relationships in order to train and control the object-based support vector machine system. In order to evaluate the proposed method, a set of test images from two different geographic regions were used for validation of the method. In each geographic region, it was attempted to select different test images (various scene features). On this basis, in the first group, three test images belong to a region in northern Iran and Bandar Anzali city, and the second group includes two images in Germany. The GSD (Ground Sampling Distance) of all the 5 test images is equal to 9 cm. Finally, the proposed method has achieved an average accuracy of 82/80% in all test images. The evaluation of the results showed that the proposed technique could be desirable as an automatic and semi-supervised method for interpreting high-resolution images of the semi-urban regions.  }, Keywords = {Remote Sensing Classification, Support Vector Machine, Knowledge-based Systems}, volume = {9}, Number = {1}, pages = {207-223}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-778-en.html}, eprint = {http://jgst.issgeac.ir/article-1-778-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {SabzaliYameqani, A. and Alesheikh, A. A.}, title = {Developing a Location Distortion Model to Improve Reverse Geocoding with Weather Data}, abstract ={Reverse geocoding is the process of assigning a readable place name or address to a point location. Common reverse geocoding methods assign the shared location to the closest venue based on Euclidean distance. In recent years, due to the advancement in positioning technology, a huge amount of spatial data has been generated by location-based social networks such as Yelp and Swarm. Additionally, various services offer the ability to provide online weather data in any coordinate and time. These data can be a valuable source of behavior patterns of different people in different weather conditions. Our study efforts to enhance the reverse geocoding based on spatial distance with the help of these data. In this way, weather condition data were used to make behavior patterns of people and check-in data were collected with the help of Swarm service. Swarm service which was used in our study is a new service from Foursquare that enables gathering check-in through the Twitter Streaming API. After gathering each check-in with Twitter streaming API, Weather data were provided instantly using the OpenWeatherMap API. Weather data were included various attributes that four of them were used in our study. Weather data were used in four categories, including; air humidity, air temperature, wind speed, and cloudiness to produce weather semantic signatures. In our study, linear, rational and sinusoidal functions were used for distorting the spatial distance with weather check-in probability in the process of reverse geocoding. In addition, two training and test data sets have been used in our case study (New York State) to specify the values of the model parameters and to evaluate the result. For the training process of location distortion functions, the check-in data were collected for New York State for one year from 01/03/2017 to 01/03/2018. The results showed that with the linear model and weather semantic signatures, the reverse geocoding results (based on spatial distance) of MRR and First Position indices (New York State) could be improved by 18.64% and 111.49%, respectively. For the process of evaluating linear location distortion function, the check-in data were collected for New York State for seven days from 01/03/2018 to 07/03/2018. The results showed that the reverse geocoding results (based on spatial distance) of MRR and First Position indices (New York State) could be improved by 13.40% and 66.96%, respectively. These results indicated the high capability of the presented model to be used outside of the timeframe of training data. In our study, one of the important challenges in the geolocation services, named the reverse geocoding process, was investigated. The model presented in this study was able to modify the distance between individuals and venues by linear location distortion function. Given that, this model has demonstrated its ability to be used with weather (and temporal) semantic signatures. It can be expected that future studies use other contextual data by location distortion functions.  }, Keywords = {Reverse Geocoding, Weather Semantic Signatures, Location Distortion Functions, Swarm}, volume = {9}, Number = {2}, pages = {1-13}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-840-en.html}, eprint = {http://jgst.issgeac.ir/article-1-840-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Mehrabi, H. and Tashayo, B.}, title = {Spatial Data Interpolation Through Recursive Solution of Moving Least Squares}, abstract ={Management and exploitation in mines require a continuous and relatively smooth surface of the mineral grades. While assessing the various mineral elements, the scattered exploratory cavities are irregularly excavated. Producing a continuous surface from measured data requires interpolation methods. Several factors, including the characteristics of the data, affect the efficiency of the interpolation methods. For this reason, the efficiency of different methods in various cases is inconsistence, and choosing the appropriate interpolation method is also challenging. Interpolation methods can be categorized into two groups of mesh-based and meshless methods. Despite the efficiency and capabilities of meshless methods, they have a fundamental shortcoming due to the fixed size of the support domain. On the one hand, the distribution of exploratory cavities in mines is usually irregular, and in some areas, it is very dense, and in others, it is very sparse. On the other hand, the grade values of minerals at the surface of the region can be very variable with high changes. Conventional interpolation methods do not have sufficient efficiency and flexibility in confronting these two aforementioned issues. In this study, a precise, reliable, and flexible method is developed for interpolation of minerals through integrating the moving least squares and recursive least squares methods. In the proposed method for crack detection, the residuals statistical test of least squares computations is used.  In this method, for the central point, a continuity threshold (non-continuity) is determined based on the standard deviation of field values, so that points with crack are revealed and removed from the calculation of the value of the central point. Moreover, the size of the support domain is determined dynamically based on the recursive property of the method. In this method, an individual radius for the support domain is assigned to each central point according to the values and distributions of the surrounding field points. The dynamic size of the support domain allows a precise and reliable estimation of polynomial coefficients and the values of the central points. The efficiency of the proposed method is evaluated by applying it to simulated data as well as comparing it with the results of conventional interpolation methods on real mineral data. The results of the simulation data indicate the ability of the proposed method to reveal the non-continuity and fractures of surfaces with determining the dynamics size of the support domain based on the data structure. To compare the results of the proposed method with conventional interpolation methods including LPI, IDW, Kriging, and RBF, the root mean square error (RMSE), mean and median of errors are used. In this way, in addition to the overall accuracy of each method, the distribution of errors is also determined. The RMSE, mean and median errors of the proposed method, using the 10-fold cross-validation method for chromium (Cr), are 28.020, 0.2.201 and 2.874, respectively, and for iron (Fe) are 1.074, 0.017 and 0.094, respectively. Comparison of these results with conventional interpolation methods indicates the efficiency of the proposed method for both groups of high concentration and significant changes in the values and low concentration and almost uniform level of values. The results indicate the ability of the proposed method in detecting the jumps and non-continuity in the support domain and removal of some field points within the dynamic process, lead to a significant increase in the efficiency of the method compared to conventional methods.}, Keywords = {Interpolation, Recursive Least Squares, Moving Least Squares, Radial Basis Functions, Mineral Data}, volume = {9}, Number = {2}, pages = {15-27}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-790-en.html}, eprint = {http://jgst.issgeac.ir/article-1-790-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Ghanbari, M. and Karimipour, F.}, title = {Study of Spatial Autocorrelation Changes Based on Newly Movement Data for the Purpose of Discovering Patterns of Human Inter-city Movement}, abstract ={The discovery of patterns of human movement in inner-city environments is one of the most important parameters in studies such as urban planning and geospatial studies. One of the sources that are widely used today to explore patterns of human movement is movement-based social media data. These media provide a huge amount of data in two dimensions of time and space. The purpose of this study is to explore and survey the hidden patterns of human inter-urban movement based on movement data derived from human daily activities in the process of sharing information on location-based social media and taking into account the semantic dimension of the data. In this study, movement data from the foursquare social media is used to provide a semantic dimension to the data. In order to discover the hidden patterns of human inter-urban movement, the capability and efficiency of spatial-temporal autocorrelation analysis have been evaluated. In this research, using statistical analysis and considering the time dimension in the first stage, a significant process of changes in the spatial-temporal autocorrelation of the studied data is revealed with respect to the urban subdivision based on Thiessen polygonization method. Secondly, the problem of the trend of spatial-temporal autocorrelation changes and the relationship between information sharing, location and urban area at different times of day, in order to extract precise intra-urban movement patterns using semantic clustering of location-based data has been examined in the most prominent patterns of urban movement in different time periods. The results of this study demonstrate the high capability of spatial-temporal autocorrelation analyzes based on the semantic dimension of movement data derived from foursquare social media in discovering hidden patterns of human movement at the urban level.  }, Keywords = {Pattern of Human Urban Movement, Foursquare Location Based Social Network, Semantic Dimension, Spatial Temporal Autocorrelation}, volume = {9}, Number = {2}, pages = {29-50}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-807-en.html}, eprint = {http://jgst.issgeac.ir/article-1-807-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Amiri, A. and AbdollahiKakroodi, A. and Ghadimi, M.}, title = {Detection Dehshir Fault Lineaments Using Radar and Optical Remote Sensing Data}, abstract ={Introduction Remote sensing satellite data has been widely used as a source of information for geologists on a regional scale. Detecting lineaments by remote sensing in desert and semi-desert areas where bedrock is fully visible can provide better results. Two types of lineaments are usually distinguishable by remote sensing data, namely: 1. Positive lineaments, including ridge and dyke bumps, and 2. Negative lineaments consisting of seams, cracks and faults. The purpose of this study is to detect the lineaments associated with the Fault, one of the active faults in central Iran, using optical, radar and altimetry data. Materials and Methods By examining the different bands of Landsat 8 satellite in order to select the appropriate band for extraction of lineaments, it was concluded that the shorter wavelength due to more penetration and better interaction with the ground surface phenomena would be more accurate. As a result, bands with wavelengths close to the wavelengths of Band 2 are used. After the necessary preprocessing, the filtering operation was performed, local sigma filtering was applied to all images (Asher, Landsat 8, DEM 12.5m and SRTM 30 m). The local sigma filter uses the local standard deviation calculated for the filter box to determine valid pixels within the filter window. This filter replaces the pixel value with the average calculated from valid pixels inside the filter box. In addition, Li filter were applied on radar images (Sentinel 1and Alos Palsar). In the present study, the automatic extraction of lineaments is based on two main calculations: first, the use of filters to detect edges, second, the information that gives us sudden changes in the value of neighboring pixels. Usually it is related to lineaments. The second stage reveals the lineaments Results and discussion In general, for fault detection, radar images are better than optical images. The DEM 12.5 m had the best accuracy among the other data sets. Among the optical images, Landsat 8 OLI sensor data with 30 m spatial resolution was more capable of fault detection. Sentinel-1 images in C band is more capable than Alos palsar L-band radar images. In the northern sections of the fault, the eastern plate of the Dehshir Fault, show an uplift. In the southern part of the fault the western plate of the fault is uplifted. The Dehshir fault moves in both horizontal and vertical directions. Conclusion In this study, using the remote sensing data (optical, radar and digital elevation model), the Dehshir Fault, which is an active strike-slip fault, is detected. Remote sensing data are particularly important in radar extraction for geological and geomorphological applications. Radar data have been able to identify fault lines in almost all parts of the area due to their better interaction with surface phenomena. Optical data is not well capable as radar images for extracting fault line. By combining remote sensing techniques with fieldwork, you can achieve desirable results with lower cost and better accuracy.}, Keywords = {Automatic Linear Extraction, Dehshir Fault, Remote Sensing, Radar}, volume = {9}, Number = {2}, pages = {51-64}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-826-en.html}, eprint = {http://jgst.issgeac.ir/article-1-826-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Bahrami, M. and Mobasheri, M. R.}, title = {Classification of Hyper Spectral Image Various Plant Classes via Coding Method in the Reflectance and its Derivatives}, abstract ={Plants paly a very important role in creating and maintaining the biological balance which is vital for the life of each living creature including humans. Due to the great importance of vegetation cover in terms of habitat, energy production and other important characteristics on the planet, the recognition and monitoring of various plant species has always been a concern for ecologists and decision makers from the economic points of view. This will be possible on a large scale with remote sensing technology and the use of satellite images containing vegetation and their classification. Different techniques of classification are deployed where in some of them, use have been made of reflectance curves and their derivatives. In some other methods, some coding system applied on reflectance curves and their derivatives are used as a fast method.  In this work, a method named CBOSE is presented in which, a coding approach on reflectance and its derivatives is applied. The CBOSE method is coding based on extreme points of the reflectance spectrum and combines from one to several bits to distinguish between plant species with relatively high spectral similarity. This coding method, after necessary pre-processing such as water vapor correction and continuum removal analysis, on AVIRIS hyperspectral images of Indian pine region containing various species such as wheat, barley, alfalfa, grass, tree, soybean and corn were also applied in three stages of germination, medium growth and full growth. Then, the features with the highest separability between the classes were extracted and the classification was done on the properties derived from the codes by selecting the training samples. The classification output of CBOSE was compared with the result of classification by classifiers Support Vector Machine (SVM), Maximum likelihood (ML), Spectral Angle Mesure (SAM), and Hamming similarity criteria and with those of field data. Also the methodology of CBOSE was evaluated and compared with those of coding methods such as Spectral analysis manager (SPAM), Spectral feature-based binary coding (SFBC), Spectral derivative feature coding (SDFC), and Spectral feature probabilistic coding) SFPC). The results show that the CBOSE methods on the average performs respectively 20, 16, 11 and 7 percent better compared to the afore-mentioned methods. Also, in order to evaluate the effects of using derivatives in the coding process, all aforementioned procedures were repeated without using derivatives in the coding processes. It showed that on the average, deployment of reflectance derivative would 8% enhances the accuracy in classification.      }, Keywords = {Coding, Derivatives, Reflectance Spectrum, Hyperspectral. Plant Classification}, volume = {9}, Number = {2}, pages = {65-75}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-812-en.html}, eprint = {http://jgst.issgeac.ir/article-1-812-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Shamsoddini, A. and Madadi, F.}, title = {Land Surface Temperature Mapping Based on Emissivity Fusion in Urban Areas}, abstract ={Land surface temperature (LST) is one of the most important variables required in environmental and climatological studies. In order to calculate LST, accurate emissivity is needed. Recently, several methods have been developed to calculate LAST and emissivity. Some of these methods estimate LST based on a pre-known emissivity, while the others calculate LST and emissivity, simultaneously. LST mapping in urban areas can be difficult due to the high variation of the land cover and the formation of mixed pixels. Accordingly, the LST calculation based on the emissivity derived from a single method can be erroneous, especially using a low spatial resolution image in the urban areas. Integration of the emissivity values derived from different methods seems to be an effective solution in this situations. In this study, LST was calculated using Split Window and Planck Law methods for Tehran city. Three different methods including classification, normalized difference vegetation index (NDVI)-based method, and normalization emissivity method were applied to derive emissivity from MODIS images. NDVI-based method is a common method used NDVI thresholding to determine the emissivity of different pixels. In classification method, each pixel is classified into one of 14 classes for which the emissivity is known. Normalization emissivity method assumes a constant value as emissivity for a pixel in different bands to calculate temperature for these bands and then the maximum temperature derived through, is used for calculation of emissivity coefficients which are used for actual LST calculation using Planck function. In addition, MODIS emissivity product (MOD11A1) was used to compare with the emissivity derived from the other methods. In order to implement this study, the remotely sensed data including Landsat-TM data acquired in 2010, and MODIS products (MOD021KM, MOD05, MOD11A1) acquired in 2012 to 2013 were downloaded. Temperature data measured by three meteorological stations around Tehran were provided to validate the results. In order to integrate the emissivity values, averaging and median methods were used to fuse the emissivity values derived from three methods and MODIS emissivity product. The results showed that NDVI-based method produces more accurate emissivity as the LST calculated based on this emissivity was more accurate than that derived from other emissivity values. Fusing the emissivity values through mean and median methods, the fused emissivity values were used for calculating LST using Planck’s equation and Split Window methods. It was shown that the fused emissivity derived from averaging method can improve the accuracy of the LST maps derived from each emissivity method. Moreover, Planck Law performed better for calculating LST using MODIS bands 31 and 32 with error of 1.6 and 1.63 Kelvin degrees, respectively, compared to that derived from Split Window method.  }, Keywords = {Land Surface Temperature Mapping, Split Window, Emissivity Fusion, MODIS}, volume = {9}, Number = {2}, pages = {77-91}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-775-en.html}, eprint = {http://jgst.issgeac.ir/article-1-775-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Zarea, A. and Mohammadzadeh, A.}, title = {Detection of Trees and Low Altitude Vegetation Cover using Aerial Imagery and LiDAR Data}, abstract ={Generating the accurate and real time information on the position of urban objects is essential for the management, planning, and automation of three-dimensional modeling of urban lands. Trees and low altitude vegetation cover (shrubs and meadows) are the most important urban objects because they play an important role in sustainable urban planning and development and environmental management and affect the urban temperature, air quality and noise levels in the urban environment. For this reason, in recent decades, identification and detection of trees low altitude vegetation cover in urban areas using remote sensing data has become one of the important research. So, in this research, a method is presented to identify trees and low altitude vegetation cover from aerial images with high spatial resolution and aerial laser scanning data. For this purpose, the first Orthorectified images of the three study areas were generated from aerial imagery and the noise in the LiDAR data was identified and eliminated. Then, Digital Elevation Model (DEM) is generated using a developed method based on the Scan Labeling Algorithm (SLA). In addition, normalized Digital Surface Model (nDSM) has been obtained by differentiating the Digital Elevation Model (DEM) from the Digital Surface Model (DSM). In the following, high and low altitude areas of the study areas have been identified by thresholding on the normalized Digital Surface Model (nDSM). Then, an Enriched Vegetation Index (EVI) in shadow areas was produced from aerial image to separate vegetation and non- vegetation areas. Eventually, trees and low altitude vegetation cover identified by overlapping the vegetation areas with high and low altitude areas, respectively. In this research, detected trees and low altitude vegetation areas evaluated by Working Group IV, Commission III of International Society for Photogrammetry and Remote Sensing (ISPRS-WGIII/4). In this study, average pixel-based completeness, correctness and quality metrics in three study areas for detected trees are 74.00%, 63.50% and 52.10%. The mentioned average metrics for detected low-altitude vegetation cover are 58.00%, 69.40%, 46.30%. The evaluation results indicates that average object-based quality metric for detected trees has highest value with respect to other methods which introduced by other researchers. Also, average pixel-based and object based completeness, correctness and quality metrics for detected trees and low altitude vegetation metrics have acceptable level than other introduced methods.  }, Keywords = {Aerial Imagery with High Spatial Resolution, LiDAR Data, Vegetation Index, Tree Detection, Low Altitude Vegetation Cover}, volume = {9}, Number = {2}, pages = {93-116}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-694-en.html}, eprint = {http://jgst.issgeac.ir/article-1-694-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Jelokhani-Niaraki, M. R. and Fazelian, M. and Navahi, F.}, title = {Evaluation of People Attitudes Toward Citizen-centered Geographic Information Systems (Case Study: District # 6 of Tehran)}, abstract ={Given the new approaches of urban management, it is necessary to change the expert-driven, closed, intra-organizational and centralized attitude of urban management to open, participatory, voluntary and citizen-centered management. It is important to note that the smart person is one of the essential requirements of the smart city and in fact the emphasis on information and communication technology and electronic life infrastructures cannot achieve the idea of a smart city without considering the smart citizen. In modern, smart urban management, citizen participation plays a prominent role. Citizen-centric Geographic Information Systems (GIS) as a new concept and approach in urban management provides powerful and effective location-based tools and platforms for real citizen participation in urban affairs. Citizen-centered GIS systems have important applications in various areas of urban management including environmental monitoring and control, crisis management, tourism development, transportation development, refurbishment and refurbishment of worn-out urban textures and so on. Today, we see the use of GIS systems and projects becoming more and more public and citizen-centric. Citizen-centered GIS systems seek concepts and technologies based on the location that enable citizens to participate in management and urban decision making. These systems provide a suite of necessary spatial analysis and analysis tools for citizen participation in identifying and reporting problems in the city, providing solutions to improve urban problems, etc. Before implementing these systems, paying attention to the attitude of citizens and their willingness to use these systems is very important. Therefore, the present study assesses the attitude of citizens towards Citizen-centered GIS and examines their willingness to participate in solving urban problems as well as receiving urban services through these systems. In other words, this study examines the level of citizens' interest in using location-based data and analysis as well as the production of data in citizen-centered GIS. In this regard, District 6 of Tehran as one of the most populated and dynamic areas in the center of Tehran was selected as the study area. The results show that 93.6% of the citizens are willing to participate in reporting urban problems, making urban decisions and urban planning. According to the research findings, 53 percent of citizens believe that media advertising is the best way to cultivate and promote the use of these systems. Among the effective factors in motivating citizens to use these systems, simplicity and attractiveness of the system with 44.6% is the most effective factor in motivating participation and use of these systems. According to the majority of citizens (56.9%), the most important obstacle in using these systems is the lack of trust in municipalities in terms of the effect of citizens' opinions and actions. Citizens’ attitude assessment shows that 93.6% of them agree and strongly agree to participate in reporting urban problems, decision making and urban planning through this systems. The results also show that 70.8% of the citizens tend to interact with different urban organizations. Providing urban services through these systems were also well received by respondents, with 89.6% of citizens agreeing and strongly agreeing to receive many urban services through these systems.    }, Keywords = {Urban management, citizen-centered GIS, citizen participation, citizens’ attitudes}, volume = {9}, Number = {2}, pages = {117-129}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-836-en.html}, eprint = {http://jgst.issgeac.ir/article-1-836-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Emami, H. and Safari, M.}, title = {A Synthesis Method Using Environmental, Geological and Remote Sensing Data in Groundwater Research: A Case Study of Saveh Plain}, abstract ={Groundwater is considered the major portion of the world’s freshwater resources. One of the main challenges facing the sustainable development of IRAN is the need for better management of its limited fresh water resources. Hydrogeological mapping of groundwater resources is one of the main tools for the controlled development of groundwater resources. Remotely sensed surface indicators of groundwater provide useful data where practical classical alternatives are not available. Integrated remote sensing and GIS are widely used in groundwater mapping. Locating potential groundwater targets is becoming more convenient, cost effective than invasive methods and efficient with the advent of a number of satellite imagery. The nature of remote sensing-based groundwater exploration is to delineate all possible features connected with localization of groundwater. Data, driven out of remote sensing, support decisions related to sustainable development and groundwater management. With increasing population and urban development as well as agriculture, attention has been paid to the management of surface and subsurface water. One of the ways to manage water resources is to identify water areas with different potentialities and exploit them according to their capacity. Today, due to the efficiency of the GIS, this tool is used to provide a variety of models and zones, at a low cost and time saving, many groundwater issues. All the information layers have been integrated through geographical information systems analysis and the groundwater potential zones have been delineated. Weighted overlay modelling technique was used to develop a groundwater potential model with three weighted and scored parameters. In the present study, a combination of remote sensing data, geographic information system and multi-criteria factors has been developed to prepare a map of susceptible groundwater in the city of Saveh. Thematic maps of each of the factors affecting groundwater, including lithology map, precipitation map, drainage map, land use map, linear Density map, Topographic map, Slope map, Aspect map and temperature map using data Landsat 8 satellite, Digital Elevation Model, geological maps, fractures, soil, land use and rainfall were used. In the next step, after preparing the Raster map these factors, According to hierarchical analysis method, each of them was assigned weight. Finally, the above-mentioned thematic maps were computed using GIS analysis using a weighted algorithm, and a groundwater potential map was obtained. Although the area is characterized by hard rock, the area has been categorized into five distinct zones—excellent, good, fair, poor, and very poor. According to the final map, 14.5% of the area has a very low potential, 7.9% has low potential, 21% has a medium potential, 34.3% has a good potential, 22.5% has a very good potential. The high potential zoning is more consistent with alluvial deposits, plum-pudding stone and coarse alluvial deposits, as well as areas that cover the lands of the garden and the shrub. No potential zone matches to maximum Elevation and other matches with areas that have volcanic rocks and Granite is. Finally, to assess the accuracy and validation of the results, the location of the wells in the study area was used. By comparing the final map and dispersion of piezometer wells, the accuracy of the method used in this study was confirmed. The results of the assessment showed that most wells exist in very good potential areas. Although some of them are also in other areas. This could be due to the fact that in these areas there are many slopes and may have been caused by soil layers in the basement and along faults and fractures that caused water outcrops in those areas. While their power supply is very good at higher potential areas.  }, Keywords = {Groundwater Potential, Geographical Information System, Remote Sensing, Analytical Hierarchy Process}, volume = {9}, Number = {2}, pages = {131-150}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-793-en.html}, eprint = {http://jgst.issgeac.ir/article-1-793-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Mousavi, S. M. and Ebadi, H. and Kiani, A.}, title = {Provide an Optimal Deep-network Method for Spectral-spatial Classifying of High Resolution Images}, abstract ={Ever-increasing growth and development of urbanization and rapid land-based changes have increased necessity of continuous checking of these changes for urban and environmental planning. Classification of remote sensing high resolution images can be the most effective way to achieve this goal. The classification of these images has always been challenged due to similarities between different classes and differences through one class. Dense classification, also known as semantic segmentation, is also one of the open issues in remote sensing domain. The existence of these kinds of challenges reminds the need for precise methods for classifying images. Deep learning, because of ability to extract deep and powerful features and compatible potential with images, has been known as a good choice in this domain. In this article, in order to cope with the challenges, a convolutional neural networks method based on deep learning is presented for classifying images. The reason for this choice is using deep and comprehensive features by the mentioned method. These features are captured in a supervised manner. In deep learning methods, on the other hand, there is an underlying need for training data and Because of restriction of data in remote sensing, it has been tried to ensure that the number of training samples used in the project is adequate. In this paper, the underlying goal is determination of CNN structure based on deep networks for effective classifying of aerial imagery with high spatial resolution. For this purpose, First, a deep network is designed to extract the deep and optimal features of the aerial image. Architecture and configuration of the deep network are defined in this step. Then, to evaluate the impact of different dimensions of neighborhoods on producing optimal deep features, feature extraction in image patches with different dimensions has been investigated. These patches have been used for train network. After training network with Patches in different sizes, Finally, in order to investigate the classification ability of the deep learning method, in a different approach, a support vector machine has been used for classification based on the deep features produced by the CNN. Comparison of the classification results shows almost same results in the deep learning method in comparison with the conventional support vector machine model, in the same conditions to using deep features. To evaluate the method, aerial data with a spatial resolution of one meter in Des moines area in USA and other data from Royan district in Mazandaran province have been used. Finally, the results of the evaluations show improvement in all three criteria including precision, recall and f1-score in the condition of using larger patches. Also, in general, using of deep learning methods as feature extractor and classifying these deep features by the support vector machine has a bit better evaluation results than feature extraction and classification by CNN.}, Keywords = {Image Classification, High Spatial Resolution Images, Feature Extraction, Deep Learning, Convolutional Neural Networks}, volume = {9}, Number = {2}, pages = {151-170}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-814-en.html}, eprint = {http://jgst.issgeac.ir/article-1-814-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Moradizaded, M. and Akbari, D.}, title = {Intelligent Fuzzy-based Feature Selection for Soil Moisture Classification}, abstract ={Despite the capability of remote sensing to direct observation of soil moisture content, the radiances measured by sensors are usually affected by different soil and atmosphere parameters. Therefore, understanding the importance of selecting the optimal features for soil moisture recognition, the application of fuzzy logic to perform intelligent feature selection is a distinguished line of research. In the following, the selected features were used in two widely used classifiers (SVM (Support Vector Machine) and MLP (Multi-Layers Perceptron) artificial neural network) in order to soil moisture classification. These classifiers were found competitive with the best available machine learning algorithms. In other words, the main purpose of this model is to select the least number of features based on fuzzy logic aligning with increasing the accuracy of soil moisture classification. The proposed method was applied and validated using observations carried out for the Iran region. In order to compare the soil moisture classification accuracy using the features selected by fuzzy-based model, a different scenario was also considered. In the latter case, vegetation cover (NDVI), soil surface temperature (LST), and topography as selected features for soil moisture classification, were entered into the above-mentioned classifiers. The reason for choosing these three features among all the features is their significant effect on the amount of soil moisture. The results obtained were very encouraging and indicated about 8% improvement on soil moisture classification accuracy using the proposed feature selection method.  }, Keywords = {Remote Sensing, Soil Moisture Classification, Intelligent Feature Selection, Fuzzy Logic, SVM, Artificial Neural Network}, volume = {9}, Number = {2}, pages = {171-180}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-803-en.html}, eprint = {http://jgst.issgeac.ir/article-1-803-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {cheraghi, E. and Maghsoudi, Y. and Salehi, M.}, title = {Crop Phenology Retrieval Using Polarimetric Signatures}, abstract ={Today, agricultural products have important role in human life. Phenology monitoring of agricultural fields by remote sensing and synthetic aperture radar is useful because it provides key information for farmers with extensive fields. This paper deals with the retrieval of phenological stages of agricultural crops by the degree of polarization (DoP), co-polarized and cross-polarized polarimetric signatures. The DoP is taken as one of the main parameters of the scattered wave, which is received polarization-basis rotation invariant. It is shown that the DoP signature provides information about the phenology that can be complementary to that provided by the conventional polarization signatures. The phenology retrieval is performed by a new approach based on the polarimetric signatures and matching parameters. In this approach, first a signature of each phenological stage is randomly selected as the reference signature. Then, using the matching parameters, the similarity values between the reference and other signatures are calculated. Finally, the phenological stages are identified by analyzing the results. The time series of RADARSAT-2 fine quad-pol images acquired over the Barrax area have been used in this study. This dataset includes a dense revisit time along the growth cycle by combining different incidence angles and different orbit passes (ascending/descending). The experimental results show the good performance of using the DoP signatures, average accuracy 63%, and the similarity between them for retrieving the phenological stages. The DoP signatures are less sensitive to the incidence angle, but more dependent on the physical characteristics of the crops. The results also demonstrate that the matching parameters based on the geometric features of signatures can provide valuable information especially for the oat crop.}, Keywords = {Agricultural, Degree of Polarization (DoP), Phenology, Polarimetric Signature, Time Series}, volume = {9}, Number = {2}, pages = {181-193}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-827-en.html}, eprint = {http://jgst.issgeac.ir/article-1-827-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {AsghariBeirami, B. and Mokhtarzade, M.}, title = {Introducing an Unsupervised Method for feature Extraction of Hyperspectral Images Using Bands Clustering in the Prototype Space}, abstract ={Hyperspectral sensors have high capability in identifying objects by acquiring a large number of adjacent electromagnetic bands. Although This large number of bands makes it possible to approximate the more precise spectral curve of the material, it also brings some challenges. The difficulty in data transfer, the weak performance of conventional statistical classifications due to the limited number of training data, and the high processing time are the most important ones. Hence, different methods of dimensionality reduction are proposed for hyperspectral images. In the following article, an unsupervised feature extraction method is proposed based on the bands clustering technique. In the proposed method, after the prior image clustering and forming the prototype space with the aid of the clusters’ averages, the bands are clustered using the K-medoids clustering algorithm. In each cluster, four types of central tendency measures, mean, geometric mean, harmonic mean, and median are used to extract the final features. The experiments are conducted on the three real hyperspectral images with medium and high spatial resolution. Final results indicate that the classification results of the proposed method can reach (72.12) which is 7% higher than the other four competitive methods, principal component analysis (PCA) (64.39), wavelet (64.58), feature selection method based on bands clustering based on variance (65.30) and non-parametric weighted features extraction (NWFE) (64.12) .}, Keywords = {Feature Extraction, Classification, Prototype Space, Clustering, Virtual Dimensionality, Maximum Likelihood Classifier}, volume = {9}, Number = {2}, pages = {195-207}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-907-en.html}, eprint = {http://jgst.issgeac.ir/article-1-907-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Karami, F. and Malek, M. R.}, title = {A Review of Similarity Measurements in Location Based Social Networks}, abstract ={With the growing trend towards a world where mobile objects are getting more and more interconnected, location information is increasingly becoming a recognized need for providing rapid and timely information to the social network users. This ability has led the way to an augmentation of existing social network sites with location-based features or the creation of new ones exclusively around geographic information. Within these Location Based Social Networks vast amounts of geographic information are allocated, which attracted the attention of researchers with various scientific backgrounds.  One of the hot topics in the field of location-based social networks is mining similarities among users in the terms of location, time and semantic. In this research, we provide a comprehensive review of the methods and criteria used to measure the similarities among the users. We have categorized the existing research areas on this subject and depict a clearer and more suitable perspective for further studies. According to the results of this study, it can be stated that researches in this field have not yet reached a proper maturity and accuracy. In addition some criteria, that applied semantic information and content data, must be studied further in the future.}, Keywords = {Location Based Social Network (LBSN), Recommender Systems, Users Similarities, Semantic Similarity}, volume = {9}, Number = {2}, pages = {209-224}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-908-en.html}, eprint = {http://jgst.issgeac.ir/article-1-908-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Khoshlahjeh, M. and Ranjbar, B. and Moghimi, A. and Beheshtifar, S. and Maghsudi, Y. and Mohammadzade, A.}, title = {An Overview of the Methods and Models Used to Identify Land use Changes Based on Remote Sensing and GIS (with Emphasis on Studies in Iran)}, abstract ={In recent decades, there have been many problems in urban management and planning due to the population growth, industrial development, urbanization, natural resource depletion and marginalization. On the other hand, with the advent of science and technology, human beings have new solutions to the aformentioned problems. Land use generally means that the use of land in the present situation, that changes over the times. In this regard, the use of satellite imagery, which is an advanced tool for monitoring environmental changes, can help us to investigate these changes. Many studies have been conducted on land use with different approaches and goals, as well as many methods for classifying images and detecting changes in applications. The present research examines various aspects of the dimensions and issues in land use change studies conducted in Iran and reviews the relevant methods. The present research examines various aspects of the dimensions and issues in land use change studies conducted in Iran and reviews the relevant methods. In each of state-of-the-art research, various algorithms and methods have been introduced and implemented that have led to various results and verifications. The correctness of each method is proportional to input data and used algorithms. In other words, we don’t say a method can be considered as the best method in the change detection compairing to other methods.}, Keywords = {Remote Sensing, Satellite Images, Image Classificasion, Change Detection, Land use, Geospatial Information Systems (GIS)}, volume = {9}, Number = {2}, pages = {225-242}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-909-en.html}, eprint = {http://jgst.issgeac.ir/article-1-909-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2019} } @article{ author = {Hejazi, S. A. and Mobasheri, M. R.}, title = {Non-destructive Method for Estimating Biomass of Plants Using Digital Camera Images}, abstract ={Abstract Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. For indirect biomass estimation, remote sensing data are used to determine agriculture species biomass using multiple regression analysis or Radiation Use Efficiency (RUE) models. In agriculture, RUE or Light Use Efficiency (LUE) is defined as dry biomass produced per unit of solar absorbed radiation or Photosynthetic Active Radiation. The LUE model needs a time series of NDVI index. Here, the lack of a few satellite images may make this time series incomplete. To overcome this deficiency, the farmer provided digital images that can be replaced for the missing satellite pixels/images that were deployed. Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial-based sensors. This study demonstrated a reliable, fast, and cost-effective approach for estimating NDVI using digital camera images. High-resolution digital images were acquired in the wheat field, and automated image processing methods were developed to segment the wheat canopy from the soil background. Exponential models for aboveground total NDVI showed acceptable precision and accuracy. Canopy cover estimated with images from digital cameras was sufficiently well correlated with satellite NDVI. Here, using a regression model, the NDVI index was estimated from the digital photographs. This method is named Digital NDVI (DNDVI). To develop this method, the relationship between the vegetation fractions (VF) obtained from the digital photos and the NDVI calculated from the satellite image of the same location were examined. For calculation of DNDVI to be used in cloudy days, the farmer is asked to supply a few photos from different parts of the farm (the number of photos depends on the size of the farm). These photos will be sent to the server where the VF values and then the averaged DNDVI will be calculated. The uncertainty of the DNDVI model in estimating biomass was 0.071 with relative RMSE of about 0.14. Next, wheat biomass was calculated using DNDVI and LUE model. The results of LUE model (and  in estimating biomass show a coefficient of determination (R2) 0.62 with an RMSE of 238 (gm-2). In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring wheat NDVI and growth status.  }, Keywords = {Digital Image, NDVI, Remote Sensing, Biomass, Wheat}, volume = {9}, Number = {3}, pages = {1-11}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-820-en.html}, eprint = {http://jgst.issgeac.ir/article-1-820-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {KianejadTejenaki, S. A. and Ebadi, H. and Mohammadzadeh, A.}, title = {Automatic Road Detection and Extraction From MultiSpectral Images Using a New Hierarchical Object-based Method}, abstract ={Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the Mean Shift Segmentation Method and the HSI Color Model for Road Detection. Initially, the multispectral images were segmented and then NDVI and NDWI spectral indices were created. In addition, the segmented images were transformed to HSI color space. Then, primary road surfaces were detected by Hue, NDVI, and NDWI spectral indices. In addition, the centerlines of roads were extracted using Voronoi diagram-based technique. After extracting of centerlines of primary roads, dangle errors were removed with emphasis on the topological rules and the lengths of dangles. In order to evaluate the proposed method, the Moonah multi-spectral Image provided by the ISPRS was used. According to the evaluation results, the parameters of completeness, accuracy and quality of the proposed method are, on average, estimated to be 98%, 84% and 84%. In addition, the results of the proposed method were compared with the results of five state of-the-art methods. The results demonstrate the high capability of the proposed method in detecting and extracting roads from satellite multispectral images in urban areas.}, Keywords = {Mean Shift Segmentation, Hue, NDVI, NDWI, Voronoi diagram}, volume = {9}, Number = {3}, pages = {13-27}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-834-en.html}, eprint = {http://jgst.issgeac.ir/article-1-834-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Abolhasani, H. and Mohammadzadeh, A.}, title = {Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms}, abstract ={acquisition field reference data using conventional methods due to limited and time-consuming data from a single tree in recent years, to generate reference data for forest studies using terrestrial laser scanner data, aerial laser scanner data, radar and Optics has become commonplace, and complete, accurate 3D data from a single tree or reference trees can be recorded. The detection and identification of tree species and their precise spatial information are essential for the management of natural or man-made forests, and urban vegetation covers. Terrestrial laser scanners are active remote sensing sensors that offer the ability for generating high-level spatial information for forestry and nature conservation applications. A terrestrial laser scanner acquire detailed tree structure even in the sub-branch level. Hence, geometric information of the trees can be obtained with high accuracy from the terrestrial laser scanner point cloud data.The proposed process in this paper is to first use the laser data points of the terrestrial laser scanner of three different tree species: Quercus_petraea oak tree, Pinus_massoniana pine tree and Erythrophleum bean tree. geometric parameters of these trees These include extracted tree height, base canopy height, canopy height, canopy volume and tree diameter profiles. For each species, there were 12 single tree point cloud data of terrestrial laser scanner that were processed by the reference paper provider and the leaves of the trees were considered as noise and deleted. After the geometrical parameters of these trees have been extracted, considering these geometrical parameters (9 geometrical parameters) as a feature and using support vector machine algorithms and nearest neighbor classification of these three tree species done. It is worth noting that the accuracy of the methods for extracting the geometric parameters of trees has been evaluated by reference data that were produced non- automatically. In classification algorithm support vector machine is implemented in MATLAB programming language and RBF kernel is used for separation of three species and from each 12 point clouds of each species 8 point clouds as training data and 4 point clouds as test data are considered. In classifying the nearest neighbor, the value of K is empirically set when the algorithm is most accurate, and same as the SVM method of the 12 clouds available, 8 clouds are considered as training data and the rest of the clouds as test. One of the prominent goals of this study is to investigate the potential of the SVM and KNN for classificaction of tree species using few geometric features and few training samples.The evaluation results indicate the acceptable achieved accuracy 81% for the SVM algorithm and 74% for the KNN algorithm. In both SVM and KNN methods the accuracy of Q. petraea is 100% because the geometrical and structural features of this species are quite different from the other two species, which is clearly visualized in the images and the difference between the two The other class is completely done. The challenge of this classification relates to the other two species because they have almost identical geometrical parameters.The classification results show that the support vector machine algorithm with less training data performs better than the nearest neighbor algorithm in separating these two tree species.  }, Keywords = {Point Cloud, Terrestrial Laser Scanner, Geometric Parameters of Tree, Tree Species, Support-vector Machine, Nearest Neighborhood}, volume = {9}, Number = {3}, pages = {29-40}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-824-en.html}, eprint = {http://jgst.issgeac.ir/article-1-824-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shirzad, R. and alesheikh, A. A. and AsgharzadehNesheli, M.}, title = {Risk Prediction of Leptospirosis by Considering Environmental Factors in Iran Using MAXENT Model}, abstract ={The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MAXENT) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10.6.1 and ENVI 5.3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MAXENT as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2,3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43.5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11.9%, NDVI with 4%, average temperature with 1.3% and displacement with 1.1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0.956 and 0.955, respectively.   }, Keywords = {Geo-spatial Information System, Leptospirosis, MAXENT Model, Spatial Modeling, Jackknife, Ecological Niche Modeling}, volume = {9}, Number = {3}, pages = {41-50}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-866-en.html}, eprint = {http://jgst.issgeac.ir/article-1-866-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ayazi, S. M. and SaadatSeresht, M.}, title = {Comprehensive Analysis of Dense Point Cloud Filtering Algorithm for Eliminating Non-Ground Features}, abstract ={Point cloud and LiDAR Filtering is removing non-ground features from digital surface model (DSM) and reaching the bare earth and DTM extraction. Various methods have been proposed by different researchers to distinguish between ground and non- ground in points cloud and LiDAR data. Most fully automated methods have a common disadvantage, and they are only effective for a particular type of surface. Also, most of these algorithms have good outcomes in simple landscapes and not suitable in complex scene. In this article, the filtering methods are divided into three groups: First: traditional methods including slope-based methods, surface-based methods, morphology methods, TIN-based method, segmentation methods and other rule based filtering methods, second: methods that have specific algorithms or improved efficiency of existing algorithms and finally third filtering techniques: based on new machine learning and deep learning techniques. Then investigate and analysis comprehensively the operational problems, their challenges and efficiency of this methods for different areas mountain, forest, urban. Identify and advantages and disadvantages of each method and suggestions for using different methods in different areas is presented. The results of this analysis indicate that the combination of improved and new methods of machine learning and deep learning are suggested in order to improve the performance of filtering techniques.}, Keywords = {Point Cloud Filtering, DTM Extraction, Machine Learning, Deep Learning}, volume = {9}, Number = {3}, pages = {51-71}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-816-en.html}, eprint = {http://jgst.issgeac.ir/article-1-816-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Barzegari, S. and Agahamohammadi, H. and Behzadi, S.}, title = {Development of a Strategy Using Spatial Analysis and Neural Network for Spatial Analysis of Water Levels at the Time of Drought}, abstract ={Urmia lake due to the presence of various species of wildlife, species of vegetation on the islands, create a natural balance in the Azerbaijan region,  tourist, recreational and social value, medical value, reserve of the Bio sepehr and as well as a wetland of international importance is special. Over the last few decades , use remote sensing technology to detect trends such changes various researchers have drawn attention to themselves. Factors that have caused Urmia lake will be in such a situation  is varied. But in general, they can be divided into two categories :The factors that played a role in humans includes free use of water resources , agriculture unbridled development around the lake, and environmental factors like climate change , which according to the reduction of heavens and evaporation of Urmia lake water And reducing the flow volume and reduce annual temperature the lake ecosystem has been affected. Study of meteorological parameters of Urmia lake and investigation of its level changes in order to apply water resources management is important. Recent studies show which level and volume of lake water relatively decreasing. Urmia lake water level from 1992 to 1997 significantly increased and decreased from 1997 to 2009 and has remained almost constant since 2010. As a result, to rebuild the lake and managing the water resources of this lake is necessary, the role of effective parameters is determined. Therefore, neural network method was used in this research,meteorological parameters such as evaporation,temperature, precipitation, and annual amounts of groundwater abstraction of  wells around the Urmia lake and the amount of water entering the lake, between 1997 and 2011, as input parameters And the annual altitude and area of the lake water entered the neural network as output parameters. In this research, the Levenberg rules were used to train the network. After training model by meteorological parameters, it was determined that the neural network model approximates the data in a perfectly accurate and accurate manner. It can also be predicted that changes in height and area occur by changing each of the parameters. This network estimates the lake area of Urmia at 3% error and 97% accuracy and lake level of 0/8 m. The correlation coefficient of the removal was obtained with the height and the range of -0.4. The correlation coefficient of precipitation with 2 dependent parameters was obtained +0.15 Input flow rate of +0.4.  After reviewing the model, it was found that the removal parameter from underground wells and the Input water volume into the lake compared to other parameters have a more significant effect on altitude and area. The results indicate that water use for agriculture and harvesting of water resources have increased And also the crops that are grown are products with a high water consumption pattern And also the water stored behind the dams has reduced the inflow to the lake.  }, Keywords = {Urmia Lake, Climate Change, Neural Network, Change in Altitude and Area}, volume = {9}, Number = {3}, pages = {73-84}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-802-en.html}, eprint = {http://jgst.issgeac.ir/article-1-802-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Bagheri, K. and NeysaniSamani, N. and JolokhaniNiyaraki, M. R.}, title = {Routing Vehicle of Urban Waste Collection Utilities GIS}, abstract ={Municipal solid waste collection is expensive and, in some cities, 46–85% of their whole waste management expenses are used for waste collection and transportation. Rapid urbanization and every day human actions generate a large amount of waste from residential, commercial, or industrial extents all over the world. Waste collection optimization can decrease the waste collection budget and environmental emissions by reducing the collection route distance. Therefore, suitable planning of waste collection process can prevent additional costs and increase the efficiency of waste management. This paper aims to find appropriate routes for municipal waste collection using geospatial data to minimize total travel time of vehicles. On the other hand, the constraint set for the maximum travel time, the maximum total distance traveled, and the maximum number of checked waste bins have to be taken into account. In this research, the integration of the Vehicle Routing Problem (VRP) with the Geographic Information System (GIS) is used. Research scenarios are considered based on a fixed non-uniform fleet with a constant number of each types of vehicles in the Asymmetric Capacitated Vehicle Routing Problem with Backhauls and with Time Windows (ACVRPTW). The reason for choosing the ACVRPTW for this research is that in urban traffic network, vehicles have limited capacity, also urban passages are oriented asymmetrically, on the other hand, the time of service to buckets garbage is important and they should be viewed in a time window. In order to implement the proposed method, first, location data of waste bins and the average amount of waste generated per day are prepared. Also, the standards and policies of the waste management organization including the number and types of vehicles with their characteristics such as capacity and fuel consumption should be provided. The amount of waste generated by each trash should be calculated further. These data provide a pattern for the amount of waste accumulated in each garbage bin. The next step is solving the routing problem; in fact, the same VRP is executed in a specific way with the restrictions, parameters, and target function of the ACVRPTW. Then, the evaluation of the results will be accomplished. ACVRPTW was surveyed in a case study of Tehran, Iran. The results of the ACVRPTW are compared with real applications, indicating a decrease of 14% and 24% in each trip and whole travel time. In addition, the results also indicate that this research clearly contains a scientific approach to urban waste collection systems, and the proposed algorithm is able to take into account the constraints that the waste management organization has put forward. Furthermore, it determined the optimal time routes for each vehicle according to its characteristics.  In fact, with this method, a network can be designed in order to reduce waste collection costs significantly. In other words, the proposed algorithm has been able to find an appropriate balance between the numbers of examined waste bins, the amount of collected waste, the mileage, and the duration time that each vehicle is serviced. It should be noted that the proposed algorithm runs in 5 minutes on average.}, Keywords = {Routing, Urban Waste, Spatial-base Algorithms, Time Window}, volume = {9}, Number = {3}, pages = {85-95}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-783-en.html}, eprint = {http://jgst.issgeac.ir/article-1-783-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Esmaeily, A. and Ashjaei, H.}, title = {A Customized Model for Assessing Urban Life Quality Based on Objective and Subjective Approaches (Case study, Qom city, District one)}, abstract ={The quality of urban life considers as a key concept for meeting the basic needs of citizens in the context of general welfare, social well-being and people's satisfaction, as well as an effective tool for evaluating public policies, ranking places and monitoring urban planning and management policies and strategies. For this purpose, urban managers need to plan a standard environment for citizens using the studied policies from urban developers which leads to a better life quality. These studies are different depending on what kind of approaches and the quality assessment methods were used. Therefore, different approaches were used to assess the urban quality life, however, there were no extensive study to consider physical, spatial and social indicators. Many researchers believe that the life quality is a multi-dimensional concept and could be expressed using objective and subjective approaches. Therefore, the main objective of this research is to measure the quality of urban life based on two objective and subjective approaches at districts level in region one of Qom city, Iran. In this way, based on the study area features and available data, two domains of accessibility and sound pollution accompany with their indicators were assessed. These lasts were done by calculating the quantitative data, performing the qualitative analysis and questioning the citizens. In fact, there is a direct relationship between life quality and district features. If these features could be spatially optimized, their access will be simplified and ultimately will have positive effect on districts' residents and their life quality will be improved. On the other hand, the factors causing sound pollution such as vehicles, crowdsourcing on the street, day-to-day construction activities, increasing industries in the vicinity of cities are reducing the quality of life. In this research, for accessibility domain, indicators like administrative, educational, commercial, health-therapeutic, sport-recreational, cultural-religious and green space were considered and for sound pollution domain, street network, urban land use and population density were considered. For extracting and modeling these ten indicators, in two principal domains of this research, the three layers of land use, street network and population were used. By producing classified maps of two domains of accessibility and sound pollution in objective and subjective dimensions, the correlation between objective and subjective outcomes was investigated. In addition, neighborhoods ranking in terms of urban life quality was assessed. The results of integrating these layers, with regard of objective approach, showed a particular pattern of life quality rate. This pattern demonstrated the highest life quality rate in city center and it decreases gradually when we distance from city center. However, the spatial analysis of statistical data showed different pattern. Finally, in order to provide a management tool, neighborhoods were ranked based on the final indicators extracted from TOPSIS method. The best sub-districts, based on objective and subjective approaches, were Nobahar and Bajak-3. One of the other objective of the study was to investigate the correlation between the results of life quality indicators in two objective and subjective dimensions. So they were analyzed separately using Pearson's correlation coefficient. The results showed a high correlation between objective and subjective results in commercial and green space indicators, and a significant positive relationship was observed in street network, administrative, cultural-religious, health-therapeutic and population density indicators, however, the other indicators demonstrated an inverse correlation. Overall, it can be concluded that the subjective results are more reliable than the objective results.}, Keywords = {Urban Life Quality, Objective, Subjective, TOPSIS, Qom City}, volume = {9}, Number = {3}, pages = {97-111}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-763-en.html}, eprint = {http://jgst.issgeac.ir/article-1-763-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Izadi, S. and Sohrabi, H. and JafariKhaledi, M.}, title = {Comparison of Geographically Weighted Regression and Regression Kriging to Estimate the Spatial Distribution of Aboveground Biomass of Zagros Forests}, abstract ={Aboveground biomass (AGB) of forests is an essential component of the global carbon cycle. Mapping above-ground biomass is important for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. Remote sensing provides wide observations to monitor forest coverage, the Landsat 8 mission provides valuable opportunities for quantifying the distribution of above-ground biomass at moderate spatial resolution across the globe. The combination of the sample plots and image data has been widely used to map forest above-ground biomass at local, regional, national, and global scales. Many predictive methods have been suggested to estimate forest aboveground biomass from sparse sampling points into continuous surfaces, ranging from regression methods such as Geographically Weighted Regression (GWR) and geostatistical methods such as Regression Kriging (RK). Researchers have been particularly interested in understanding the causes and effects in ecosystem functions of spatial autocorrelation and heterogeneity, over the past decade. Where in forestry data include spatial autocorrelation and heterogeneity, the independence and homogeneity assumptions of standard statistical approaches, such as ordinary least squares (OLS), may be violated. Many spatial models (such as Geographically Weighted Regression and Regression Kriging) have been developed in recent years to discuss spatial effects in the relationships between variables. Spatial models can be divided into global and local models depending on the spatial scales used in the modeling process. A global model usually involves, a tool to model spatial autocorrelation between observations in neighboring locations, through either a covariance matrix that can be calculated using a variogram or spatial weight matrix based on neighborhood proximity. Global models, of course, do not well represent spatial differences at any given location and may not be successful in dealing with spatial heterogeneity. By comparison, local models, such as geographically weighted regression, adequate a regression relationship within a given bandwidth for each spatial location using the neighbors. From the relationships between variables, the local models are more useful in exploring locational spatial variation (heterogeneity). In the present study, using a Landsat 8-OLI image, and Geographically Weighted Regression and Regression Kriging modeling were compared for the estimation of aboveground forest biomass. In this study, we gathered aboveground biomass data from a total of 184 (30 × 30 m) sample plots in Zagros forests in the Kohgiluyeh and Boyer-Ahmad Province. The datasets corresponded to the Landsat 8 image pixel values. We applied the species-specific allometric equations for individual trees to estimate forest aboveground biomass. The aboveground biomass at plot-level is simply the summation for all trees within the same plot. The estimates were evaluated by ten-fold cross-validation and performances of the model was evaluated using the coefficient of determination (R2) and relative root mean squared error (RMSE%). The efficiency of the predictions can be described with the scatterplots showing the relationships between the forest above-ground biomass estimates and reference data. Results showed 1) that Geographically Weighted Regression (R2 = 0.61, RMSE%= 22) was a fairly better approach and could provide promising results for the prediction of forest above-ground biomass compared to Regression Kriging (R2 = 0.47, RMSE%= 28) and 2) scatterplots depicted that the problems of overestimation and underestimation for all the prediction were apparent.}, Keywords = {Spatial Heterogeneity, Spatial Autocorrelation, Forest Modelling}, volume = {9}, Number = {3}, pages = {113-124}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-879-en.html}, eprint = {http://jgst.issgeac.ir/article-1-879-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Saadatzadeh, E. and Aliabbaspour, R. and Chehreghan, A. R.}, title = {Improvement of the Effective Components in the PDR Positioning Method Based on Detecting the User’s Movement Mode Using Smartphone Sensors}, abstract ={The purpose of this paper is to evaluate and improve the accuracy of indoor positioning using smartphone sensors based on Pedestrian Dead Reckoning (PDR) method. In some specific situations, such as fires or power outages that disable infrastructure-based positioning techniques, using PDR method based on smartphone sensors that perform positioning continuously is a good solution.This paper focuses on determination of the user’s movement type to evaluate effective components of indoor positioning method. First, movement samples are evaluated with the feature-vectors of data from sensors and three classification algorithms (Decision Trees (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN)). From the perspective of feature-vectors, the proposed features significantly improve the performance of three classification algorithms compared to previous research features. From the perspective of classification algorithm also Support Vector Machine had best performance with %99.3 accuracy, while spending the most time.  In the second phase, step detection is performed for norm acceleration values based on the definition of the upper and lower threshold and the time threshold. The directional component is also obtained by combining accelerometers, magnetometer and gyroscope sensors. Localization tests were performed while the user holding the phone in front of him with two states (normal walking, running) in three paths of different geometry (squares, circles and rectangles). The final accuracy obtained from normal walking test for three paths of square, circular and rectangular shapes was %4.8, %3.6, and %2, respectively. The final accuracy of the running mode was also obtained for three paths of square, circular and rectangular shapes equal to %8.4, %5.7, and %4, respectively.}, Keywords = {Indoor Positioning, Detecting The User’s Movement Mode, Pedestrian Dead Reckoning, Smartphone Sensors}, volume = {9}, Number = {3}, pages = {125-144}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-856-en.html}, eprint = {http://jgst.issgeac.ir/article-1-856-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shahmoradi, A. and Behzadi, S.}, title = {Optimum Routing in the Urban Transportation Network by Integrating Genetic Meta-heuristic (GA) and Tabu Search (Ts) Algorithms}, abstract ={Urban transportation is one of the most important issues of urban life especially in big cities. Urban development, and subsequently the increase of routes and communications, make the role of transportation science more pronounced. The shortest path problem in a network is one of the most basic network analysis issues. In fact, finding answers to this question is necessity for higher level analysis. In general, shortest path solution methods using optimization algorithms are divided into two categories: exact and approximate algorithms. In exact algorithms, achieving the optimal solution requires time, and consequently more cost. On the opposite side, there are some approximate algorithms that work in a short period of time. Meta-heuristic algorithms are among approximate algorithms that are capable of finding optimal or near-optimal solutions in a reasonable period of time. The method used in this study is to solve the shortest path problem with the combination of Genetic meta-heuristic (GA) and Tabu Search (TS) algorithms. GA is inspired by genetic science and Darwin's theory of evolution; it is based on survival of the highest or natural selection. A common use of genetic algorithms is to be used as an optimization function. In GA, the genetic evolution of living things of life is simulated. Inspired by the evolutionary process of nature, these algorithms solve problems. GA forms a set of population (solutions), then it achieves an optimal set by acting some possess on the correct set. To solve a problem by genetic algorithms, it is necessary the problem is converted to the specific form required by GA. On the other hand, TS algorithm is not population-based. It obtains an answer, then it tries to direct the answer to the optimal solution by applying a series of operators. This algorithm is highly similar to the Simulated Annealing algorithm. In this paper, for solving the shortest path problem, a series of geometric pre-processing on the network is done to generate a search area around the source and destination nodes. In the proposed algorithm, the cost function is defined as a complex number, which the real part shows the sum of the weight of the real edges, and the imaginary part denotes the number of virtual edges. The innovation of this research is about applying Tabu Search algorithm in mutations process of genetic algorithm. The proposed method overcomes the inappropriate response of the pure genetic algorithm in terms of the final weight of the path especially the large networks. In order to evaluate the efficiency of the proposed algorithm, the algorithm was implemented on a real directional network which is part of Tehran city road networks including 739 nodes and 1160 edges. The results show that in the proposed algorithm, the length of the path is as close as possible to the solution obtained from the definitive Dijkstra’s algorithm. This algorithm predicts approximately the final path length of 5% more than Dijkstra’s algorithm. But in terms of running speed, it is 5.12 times faster than the Dijkstra’s algorithm. In comparison with the pure genetic algorithm, the proposed algorithm is 9% shorter in average in terms of path length. And about the running time, the speed of the proposed algorithm is approximately equal to the pure genetic algorithm. Regarding to repeatability, the proposed algorithm also shows 25.36% of repeatability.  }, Keywords = {Finding the Shortest Path, Genetic Algorithm, Tabu Search Algorithm, Geometric pre-processing, Search Extent}, volume = {9}, Number = {3}, pages = {145-158}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-862-en.html}, eprint = {http://jgst.issgeac.ir/article-1-862-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Rezaei, M. and Hosseinali, F. and Sharifi, A. R.}, title = {Evaluating the Potential Area for Constructing Photovoltaic Power Plant in Iran using Fuzzy Approach}, abstract ={Solar energy is an important source of renewable, sustainable, and accessible kind of energy in the world. Halting environmental degradation, this kind of energy is what can reduce the damaging consequences of fossil fuels. Iran or other counties located in the arid area can use this type of energy. In fact, the geographical position of Iran in word's dry belt makes this country a proper land for constructing facilities of using solar energy. One of the most usual facilities, which use solar energy are photovoltaic power plants. Photovoltaic power plants need special situations to get the best efficiency. First of all, they need the highest portion of sunny days in a year. Moreover, to reduce the cost of construction, they must be built near the roads and power transmission lines. To find the places which include the best conditions, GIS is a useful system. GIS is able to prepare maps of the desired conditions and then combine them using various approaches to find the best locations. Many researches have already been done around the world for site selection of photovoltaic power plants. There are some researches in Iran, as well. However, the criteria used in these researches seems not to be comprehensive enough for such a sensitive site selection. Thus, this research aims to take as much as possible criteria for site selection of photovoltaic power plants into account.     The goal of this research is to find the potential area for constructing photovoltaic power plants in Iran. This purpose contains four main sections such as assessing effective factors on the operation of photovoltaic power plants, preparing factor maps (criteria maps) in GIS environment regarding the spatial nature of the effective factors, the maps conversion into fuzzy form and proper combination, and finally, classification of result into four levels. Using fuzzy logic helps us to consider unavoidable uncertainty that exists in spatial data. In this study, the prepared factor maps are first converted into a fuzzy form using fuzzy membership functions. Then, using fuzzy overlay, the maps are combined. To set the most strict situation, among the fuzzy logical operators, fuzzy AND was selected. Thus, we will be sure that the final selected sites will have the best possible conditions. The results indicate that six provinces, including Kerman, South Khorasan, Fars, Yazd, Hormozgan, and Sistan-o-Baluchestan, achieved the highest score for constructing photovoltaic power plants in Iran. Overlay, more than 557000 square kilometers of Iran have a high potential for gaining solar energy. The results also show that north and north-west of Iran in compare to the other areas, achieve less suitability for constructing photovoltaic power plants. The most important criterion that causes these situations is the portions of sunny days. While in the north side of Iran, as well as the mountainous areas such as Zagros Mountains, the precipitation is higher than the other regions, the number of sunny days in a year is less. This study detects the proper sites in a general form. The scale of the study covers the whole country. Thus, most fine-scale studies must be performed on the selected regions to find the best specific sites.}, Keywords = {Solar Energy, Photovoltaic Power Plant, Iran, Site Selection, Fuzzy}, volume = {9}, Number = {3}, pages = {159-171}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-842-en.html}, eprint = {http://jgst.issgeac.ir/article-1-842-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shahbaz, R. and Malek, M. R.}, title = {A Review On Map-Based Indoor Positioning Techniques}, abstract ={In this study, a review of indoor positioning methods based on indoor environment models is presented. Researchers introduce a variety of methods in this field. The purpose of this study is to present different methods and evaluate them. Each method is described from the point of view of the use of the spatial models. In the end, methods advantages and disadvantages have been introduced. Due to the wide variety of methods and Principles used in this field, the choice of an optimal method is not possible. Because each one has a different performance depending on the indoor environment and problem. It seems that, if it is possible, a combination of methods is an effective solution, because in some cases the advantages of a method cover the disadvantages of another method.}, Keywords = {Indoor Positioning, Map Matching, Grid Model, Graph Model}, volume = {9}, Number = {3}, pages = {173-184}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-931-en.html}, eprint = {http://jgst.issgeac.ir/article-1-931-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Effati, M. and Asgari, A.}, title = {Developing a Model Based on Geospatial Information Systems (GIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Providing the Spatial Distribution Map of Landslide Risk. Case Study: Alborz Province}, abstract ={Landslide is one of these natural hazards which causes a great amount of financial and human damage annually allover the world. Accordingly, identification of areas with landslide threat for implementation of preventive measures in order to confront against the instability of hillsides for reduction of potential threats and related risks is very important. In this research a new method for classification of landslide risk according to geographical analysis and uncertainty modeling is presented which is based on data mining in previous events. In order to do so, adaptive neuro-fuzzy algorithm which is adjusted by means of sensitivity analysis is used in inferential basis of proposed model, which analyze landside risk efficiently. The selected region for this study is available lands in Alborz province. In proposed method factors like altitude, petrology, gradient, gradient direction, distance to fault and rainfall which are some of the most serious causes of hillside's instability had been inserted and their raster maps produced in GIS context and stored in georeference database. In the next step, areas prone to landslide had been identified according to findings of proposed model and finally in addition to model evaluation according to validation outputs, another round of validation is done by field monitoring of hih-risk regions and interpretation of provided 3D models. Results show that the proposed model with root mean square error of 0.819 and correlation factor of 0.934 has a relatively high accuracy in classification of landslide risk. In addition in landslide risk geographical distribution map inside studied region, the area of landslide-prone area is the highest with respect to total area of province which shows high-risk of Alborz province against landslides.}, Keywords = {Landslide, Soft Computing, Fuzzy Inference System, Zoning}, volume = {9}, Number = {3}, pages = {185-200}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-932-en.html}, eprint = {http://jgst.issgeac.ir/article-1-932-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Mirzapour, H. and Arshia, A. and Tahmasebipour, N.}, title = {Evaluating the Performance of Geomod Model, SimWeight and MLP Algorithms in Urban Development Simulation (Case Study: Khorramabad County)}, abstract ={Recently, increasing population rate and urbanization growth, made the significance of land use more to double; So that the plan of land use in the cities has been encountered with vast imposed changes. In order to represent those changes, this study aims to model the land use changes, as an example in Khorramabad city, Lorstan province, Iran. In this regard, the raw Satellite images which captured by Landsat TM, ETM+ and OLI sensor images, corresponding to three decades of 1995, 2005 and 2015, were used. The city maps were then obtained through image processing including image geometric, omission and radiometric corrections and also implementing maximum likelihood classification methods on the images of the years studied. Then we investigated and compared different approaches for modeling, considering affecting parameters on urban development, as the simulation accuracy criteria, including: distance from the river, distance from the road, distance from the village, slope, direction, height and urban land-use in the base year. The different simulation approaches are including: the GEOMOD model, Sim Weight based learning algorithm and artificial neural network (MLP). GEOMOD selects the location of network cells according some rules: 1) resistance: simulating route of changes.  2) Regional categorization: simulating land use changes in a series of regions as the category. 3) Neighborhood instruction.  4) Providing a scale map: in the GEOMOD model, before implementation of the modeling process a scale map must be prepared; this map is used for simulating the change from one category to the other until the model imposes changes based on the map. For implementing the GEOMOD model, two images are required or even one image can be used, and instead of the second image, we can substitute the area extent of the considered land-use in the second image. The artificial neural network is a powerful tool for creating models, especially when the relationship between the infrastructural data is unknown or latent. In a multilayer perceptron artificial neural network model, the transform potential map is derived from implementation then based on Markov chain theory the model of future is estimated. SimWeight is a learning machine which is simpler than the multilayer perceptron neural network. The logic structure of  Sim Weight performs based on the nearest neighbor algorithm with the difference that Euclidean distance from the specified samples of categories are weighted. After introducing the parameters affecting the LMC program (included in the terSet software), by implementing SimWeight algorithm, the weights are determined and assigned to each of parameters. At the prediction stage new weighted parameters and specify the future model using Markov chain algorithm. The necessity of using any kind of topic information is the knowledge of accuracy. Accuracy of information is in fact the probability of information accuracy. In executive projects which consider the comparison of accuracy, the Kappa index is often used as accuracy criteria, because it takes into account false classified pixels. In the present research, the simulation performed using the mentioned methods. Finally, for validation, the simulated maps and the real ground map was matched with each other. The results reveal the GEOMOD model, SimWeight based learning algorithm and MLP algorithm have the kappa coefficient of 0.79, 0.77 and 0.72, respectively. Hence, the GEOMOD for the urban development simulation in Khorramabad has better performance than the other models. Therefore preferred GEOMOD model to predict urban development recommended to all managers, Municipal authorities and affair planners.}, Keywords = {Urban Development, Geomod, Maximum Likelihood, Neural Network, Simulation}, volume = {9}, Number = {3}, pages = {201-215}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-933-en.html}, eprint = {http://jgst.issgeac.ir/article-1-933-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shahcheragh, S. F. and Tashayo, B.}, title = {Developing a ChatBot to Answer Spatial Queries for use in Location-based Services}, abstract ={A Chat Bot is an automated operator that can interact with customers like a human operator, answer their questions, solve problems and get feedback. Real-time responsiveness, the sense of talking to a human user is one of their good features that can be used to deliver location-based services. This paper designed a Chat Bot that can talk and answer users' questions based on their location. This Chat Bot is a 24-hour smart system that Without fatigue, and the influence of environmental stimuli Provides service to users. Users who use this Chat Bot have 98% satisfaction compared to other available services. Conversational text chat capabilities, quick response, ease of use and a sense of human interaction are among the reasons for the satisfaction of the Chat Bot users.  }, Keywords = {Location Based Services, LBS,Spatial Bot, ChatBot}, volume = {9}, Number = {3}, pages = {217-224}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-934-en.html}, eprint = {http://jgst.issgeac.ir/article-1-934-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Javidaneh, A. and Karimipour, F.}, title = {An Approach for Automatic Matching of Descriptive Addresses}, abstract ={Address matching (also called geocoding) is an applied spatial analysis which is frequently used in everyday life. Almost all desktop and web-based GIS environments are equipped with a module to match the addresses expressed in pre-defined standard formats on the map. It is an essential prerequisite for many of the functionalities provided by location-based services (e.g. car navigation). Several methods have been proposed for address matching which assume a standard for-mat for the components of the address, and propose solutions for matching the known address components to map components. The situation is, however, more difficult when there is no addressing standard, and addresses are expressed in descriptive languages. An interesting example is Iran, where people express ad-dresses as a sequence of spatial elements (e.g. streets, squares, landmarks, etc.), starting from a known element. Although this method of addressing may seem very unpleasant at first, it is very efficient, because (1) it not only specifies the destination, but it also tells how to reach it. In other words, you do not need any map, navigation system, etc. to find the destination. Instead, you can reach the known starting point and then look for the next components, step-by-step; and (2) this way, you will inevitably be exposed to the environment and its spatial elements, which helps you in building up your cognitive map. This article proposes a solution for automatic matching of descriptive addresses. The solution has two main steps. In the first step, the address is decomposed to its addressing components by defining a formal grammar. For this, we assume that a descriptive address consists of two types of components: Geo-names (GN) Constant geo-names (cGN): avenue, street, alley, etc. Variable geo-names (vGN): names of the constant geo-names Spatial relations (SR): after, before, etc. In the second step, a cognitively-enriched data model is developed for interpretation of the addressing components resulted from the previous step. We use "Paths”, “Edges”, “Landmarks”, “Nodes”, and “Districts“ as the basic types of the components of a descriptive address. To perform the matching, each component of the address is assigned one of the above basic types and a search is carried out to match the each component to a single location}, Keywords = {Address-matching, Addressing System, Descriptive Address, Spatial Cognition, Natural Language Processing}, volume = {9}, Number = {4}, pages = {1-17}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-569-en.html}, eprint = {http://jgst.issgeac.ir/article-1-569-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Mokhtari, M. and Taleai, M.}, title = {Simulation of Smoke Emission from Fires in High-Rise Buildings Using the 3D Model Generated from 2-Dimensional Cadastral Data}, abstract ={Having a 3-Dimensional model of high-rise buildings can be used in disaster management such as fire cases to reduce casualties. The fundamental dilemma in 3D building modeling is the unavailability of suitable data sources. However, available cadastral 2D maps could be used as low-cost and attainable resources for 3D building modeling. Smoke will be a great threat to people's health during a fire in a building and its movement and diffusion in different parts of the building are affected by the architectural design of the building. Computer simulation can be utilized to investigate smoke movement and its emanated toxic gases under various scenarios which can play a significant role in decision making during a fires incident event to reduce human casualties. In this research, at first, 2D cadastral maps of a high-rise building located at Tehran were utilized to produce a 3D model of building with adequate details in CityEngine. Next, smoke emission in the building was simulated under seven scenarios, using PyroSim software. The two-dimensional data of apartment separation is readily available and at the lowest cost. Using some tools and techniques in spatial information systems, making it possible to utilize this data to implement a three-dimensional model of a building with details to simulate the spread of smoke from fires. The process adopted in this study made it possible to use a two-dimensional floor plan to produce a three-dimensional model of the building at the LoD4. The results confirm the appropriateness of the constructed 3D model to simulate smoke emission under various scenarios in the high-rise building. This result shows the effect and importance of smoke transfer routes, both inside and outside the building, on the emission of toxic gases. The results show that ensuring that the ducts are closed is one of the factors controlling the spread of smoke to other floors. Flue gutters and ducts are among the most important routes for emitting smoke in high-rise buildings. The simulation results of Scenario 4 show that 40 seconds after the start of the fire, the toxic fumes spread through the stairs at all top floors of the building. Although data from 2D cadastral maps can be used to create a three-dimensional model of a building, these data have shortcomings. The part related to the texture of the map features is faced with a lack of information such as the thickness of the walls, the type of used materials, the exact location of the components (doors and windows) on the walls, and so on. These factors affect the smoke emission simulation results. As a result, development of a three-dimensional model of the building containing more detailed information about the specifications and materials used in the walls and various parts of the building needs to be considered in future research. The smoke emission simulation results can also be used in a variety of applications, including emergency evacuation modeling in the event of a fire in a building, along with the inclusion of other information such as number and physical characteristics of residents in different parts of the building.}, Keywords = {High-Rise Buildings, Disaster Management, 3D Modeling, BIM, Fire Smoke, CityEngine, PyroSim}, volume = {9}, Number = {4}, pages = {19-37}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-875-en.html}, eprint = {http://jgst.issgeac.ir/article-1-875-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Hosseinpour, M. and Malek, M. R.}, title = {The Influence of Location on Nodes’ Centrality in Location-Based Social Networks}, abstract ={Nowadays, due to the widespread use of social networks, they can be used as a convenient, low-cost, and affordable tool for disseminating all kinds of information and data among the massive users of these networks. Issues such as marketing for new products, informing the public in critical situations, and disseminating medical and technological innovations are topics that have been considered by the owners of companies as well as government and private organizations. In order to achieve this goal, the methods of maximizing the speed and quality of information dissemination in social networks have been studied by researchers in various fields of science. One of the best ways to increase the speed of information diffusion is to take advantage of the social status of influential node. The role of some people within the networks is more prominent than the others, who are known by titles such as leader, important, influential, central, and vital. These people having the centrality in the social networks can have the greatest impact on the network. Identifying these people allows us to control the way of information dissemination, the spread of diseases, the more effective advertising of commercial products, or the identification of the head of different social groups. Different measures have been proposed to identify these people and to measure the centrality of nodes in a network. Identifying influential people in a network is not easy task, and the criteria for being important are varied. Therefore, there is no general index that best determines the importance of people in a network, and this index varies from one network to another and from one situation to another. In addition, whether these indexes are local or global can be a point of contention and it can produce different concepts of centrality. This paper aims to provide a new measure so-called “socio-spatial centrality” to find the central nodes in the location-based social networks, which is a linear combination of social centrality and spatial centrality. In the new proposed measure, the central node is the one that, while having more direct neighbors than the other nodes, its neighboring nodes will have a uniform and proper spatial dispersion within the given area. Degree centrality has been used to determine the social centrality and the standard distance is used to calculate the spatial centrality. By simultaneously assessing the nodes' degree and the spatial dispersion of their neighbors, the top-k nodes are identified as influential nodes. The proposed measure, along with the existing methods for measuring centralities such as degree centrality, betweenness centrality, and closeness centrality, has been applied to two real location-based social networks. The results show that the geographic coverage of the selected nodes by the socio-spatial centrality was the highest in comparison to other centrality measures, and the correlation between the output of the new centrality measure as well as the conventional centrality measures is decreasing with the increase in the number of selected nodes. Overall, evaluations show the positive impact of the proposed method on the geographic coverage maximization in location-based social networks.}, Keywords = {Node Centrality, Information Diffusion, Influence Maximization, Geographic Coverage, Location-Based Social Networks}, volume = {9}, Number = {4}, pages = {39-50}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-853-en.html}, eprint = {http://jgst.issgeac.ir/article-1-853-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ghaderi, B. and Azizi, Z.}, title = {Monitoring of dust phenomenon and Investigate its correlation with temperature and humidity parameters‌ (Case study: Khuzestan Province)}, abstract ={Dust is a phenomenon in arid and semi-arid areas due to high wind speeds on the un-polluted soil surface and prone to erosion. The occurrence of dust has always been a problem for residents in the western and southwestern regions of the country, has gradually spread to other provinces, including the capital. The study area is southwest of Iran, Khuzestan province. The latitude and longitude of the study area is, 47° 50´ E and 30° 33´ N respectively and is considered the centerm for Iranian oil and gas production. Khuzestan is the fifth most populous city in Iran. Ahvaz is the capital of Khuzestan province. Negative consequences of this phenomenon include damage caused by pests and diseases, increased road accidents due to reduced visibility and failure of this risk in human life. And retinal detachment, skin allergies, and so on. In the environment, water pollution is affected by the process of plant photosynthesis and reduced yield. The phenomenon of dust usually occurs in arid and semi-arid regions. In order to investigate the dust phenomenon in Khuzestan province using synoptic data and remote sensing data, the dust days of 20 synoptic stations were analyzed on a daily basis and from 2010 to 2017. MOD021KM product material data were obtained in accordance with the date of the dust days announced at the station, to identify dust phenomena, visual interpretation method, TDI dust parameters and D parameter. After detection of dust, the correlation between the data with horizontal visibility less than 1000 m and the meteorological parameters of relative temperature and relative humidity were evaluated by regression method. Finally, it shows the correlation and relationship between dust characteristics and meteorological parameters (temperature and humidity). According to Figures 9-10-11-12, it is possible to determine which dust index (TDI, D) has the highest and lowest correlation with meteorological parameters (temperature, humidity). Due to the fact that the data are related to the warm seasons of the year (spring and summer) And temperature changes are less volatile. The above figures show that there is the least correlation between dust indicators and synoptic parameters. The highest correlation in the TDI index with relative humidity is about 0.22. Also, the correlation coefficient of the other indicators with TDI meteorological parameters with temperature is 6.9, parameter D with temperature is 0.9, parameter D with humidity is 11.9. Therefore, there is a weak relationship between dust and temperature and humidity parameters. Correlation and regression analysis showed that meteorological parameters had the least correlation (R²) with dust indices. The results also show that TDI index compared to parameter D has the best performance in detecting dust phenomenon. And by examining the temperature, humidity and correlation with the TDI and D indices, the dust was not of domestic origin and was transient. By examining the temperature and humidity parameters and their correlation with TDI and D indicators, we came to the conclusion that dust did not have an internal origin and was transient, and also there is no correlation between dust index and meteorological parameter. According to previous studies, Iraq, Saudi Arabia, and Syria have the most dust, despite the hot, dry climate, with most of the dust coming to Iran coming from neighboring countries.}, Keywords = {Dust Phenomenon, Synoptic Station, , Statistical Algorithm, Correlation , MODIS Data}, volume = {9}, Number = {4}, pages = {51-60}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-884-en.html}, eprint = {http://jgst.issgeac.ir/article-1-884-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Fallahi, GH. R. and MolaviVardanjani, M.}, title = {study bird’s migration by using remote sensing and Geographic Information System. (case study of bird’s migration in the alikhan wetland)}, abstract ={of this Research is the study and evaluation of the relationship between vegetation area and water area with the number of birds in Band-e-Alikhan wetland between 2011 and 2015. Due to the importance of Band-e-Alikhan wetland as a seasonal wetland with fresh water, which is located 35 km south of Varamin city, it has been used as a study area. In the present study, first, the necessary pre-processing, ie radiometric corrections, which include atmospheric corrections, banding errors, etc., were performed on Landsat 7 and 8 images, and then the normalized differential plant index maps (NDVI) and water index. Normalized difference (NDWI) was generated using pre-processed images. Then, by examining the histogram of plant and water indicators produced for each year, the appropriate threshold for each index was selected and by limiting the threshold on plant and water indicators produced, vegetation and water area maps were obtained for the year, respectively. . Each year, the maps obtained by the reference data selected manually in this study by satellite imagery were evaluated, the results of which show that the average overall accuracy and capability coefficient of vegetation surface extraction and of water over five years, with89/37% and 81/89%, which demonstrates the successful extraction of these parameters are. By obtaining the effective parameters, ie the level of vegetation and water, between the number of birds and the effective parameters of linear regression, two variables were formed and its coefficients were estimated by the least squares method. The results of studies and evaluations show that the model and regression formed between the number of birds and the parameters of vegetation surface and water area have a coefficient of determination of 0.861 and an adjusted coefficient of determination of 0.582 and indicate the appropriate fit of regression. Then, by the obtained coefficients and regression, the number of birds in 1394 was estimated using the level of vegetation and water area of the same year and was compared with the actual amount of birds in 1394, the results of which showed that the bird population is 93 . 1. It is predicted. The results also show that changes in the environmental conditions of the wetland provide a clear response to changes in bird populations and bird migration.}, Keywords = {Vegetation, Water Bodies, Wetlands Band NDWI, Alikhan, NDVI, Linear Regression, Migrating Birds}, volume = {9}, Number = {4}, pages = {61-78}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-644-en.html}, eprint = {http://jgst.issgeac.ir/article-1-644-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Vahidnia, M. H.}, title = {Extending the Qualitative Trajectory Calculus Based on the Concept of Accessibility of Moving Objects in the Paths}, abstract ={Qualitative spatial representation and reasoning are among the important capabilities in intelligent geospatial information system development. Although a large contribution to the study of moving objects has been attributed to the quantitative use and analysis of data, such calculations are ineffective when there is little inaccurate data on position and geometry or when explicitly explaining events rather than presenting numerical results is needed. In some cases, the computational complexity of quantitative methods is a factor in their weakness. In such a situation, the ideas of expressing and presenting moving objects relationships in qualitative and explicit forms and using logical and rational methods instead of computational and analytical ones have been proposed. This paper presents the application and extension of the remarkable Qualitative Trajectory Calculus (QTC) framework to represent and reason about moving point objects in GIS network paths by considering the concept of accessibility. QTC extension and reasoning paradigm were theoretically defined based on basic relationships such as moving one object toward another, moving one object away from another, moving one object following on another, and so on, allowing one object to access another along a specified path. The important properties of the developed methodology were discussed by examining the conceptual neighborhood of relationships and introducing and proving a number of inference rules. The obtained rules, which are mainly presented in the form of a composition table, formed a knowledge base for logical inference. Also, some of the extracted relationships showed features such as being inverse, symmetric, and transitive. For the purpose of practical implementation, examples of inference and query were tested by employing Prolog language to develop a deduction system using logical rules and facts. In this implementation, eight moving agents were considered. The existing facts of the spatial and movement situations complementing the knowledge base were expressed in terms of seven logical sentences. In addition to these facts, five items from the whole derivation rules, defined in the theoretical part, were selected and rewritten in the form of first-order logic. According to these facts and derivation rules, some example queries were proposed including 1. Which of the following agents does agent A have access to? 2. To which agents do agent A move? 3. What agents does agent A move away from? 4. Does agent A reach agent D? 5. Does agent A move away from agent H? The SWI Prolog system was then used to answer the queries. The output of the queries from the logical inference system was compared with the output of the queries from a conventional relational database. Due to the inference capability, the logical inference system provided results that were not recoverable from the initial information in the database. For example, a regular survey of a database found that only agent B was available for agent A, but the results of the inference on the knowledge base showed that in addition to B, the factors E, C, and D were also available for A. Comparing the results of queries from the above framework with the output of the contemporary relational databases enhanced the findings of this research eventually.}, Keywords = {Qualitative Spatial Reasoning, Moving Objects, First-order Logic, Logical Deduction System}, volume = {9}, Number = {4}, pages = {79-92}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-888-en.html}, eprint = {http://jgst.issgeac.ir/article-1-888-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Yazdannik, M. and AliAbaspour, R. and Chehreghan, A. R.}, title = {Analytical Comparison of Methods for Calculating the Completeness of VGI}, abstract ={Spatial data, which is one of the main needs of human societies from business organizations to the general users today, cannot meet the needs of a wide range of users without changing the structure of conventional methods of data registration and updating on a metropolitan scale. Open Street Map, as one of the most successful implementations of the crowdsourcing approach to spatial data with the participation of the public users as the millions of scattered sensors in the environment, has provided a solution to achieve several goals such as improvement in rapid data collection and coverage of areas. Apart from the numerous benefits of volunteered geographic information, the reliance of this information solely on the participation and activity of users challenges the use of this data in various applications. However, this problem is exacerbated by the lack of the necessary frameworks for entering incorrect data, different levels of user knowledge, and different methods of data entry. Among the quality parameters, the completeness parameter, which provides the presence of one data set compared to another data set and shows the degree of importance of the features from the users' point of view, was discussed in the present study. It is also important to address this aspect of the quality of VGI in terms of its impact on the other quality parameters. In this study, after introducing the common approaches in evaluating this element of data quality and comparing the results of methods, the strengths and limitations of each method are also explained. In the general classification, the completeness parameter is examined by two region-based and the object-based approaches. After comparing results, the necessary pre-processing, and the analysis time of each method, the findings of this study demonstrate the high speed of evaluation of the region-based approach and the higher accuracy of object-based methods. Moreover, the mentioned advantage of region-based approach is achieved if there is no topological error in the VGI and there are correspondence relationships between the uses of both data sets. On the contrary, the object-based methods despite the high calculation due to the matching process; If they use the appropriate matching algorithm and guarantee the positional accuracy, they will provide more accurate results for the completeness parameter. Moreover, the object-based methods, with the possibility of calculating the omission and commission of the VGI data set compared to the official data set, provide a more accurate understanding of the completeness parameter. In the region-based approach, it is not possible to evaluate the completeness parameter separately due to the combination of these two sub-sectors. In the region-based approach, evaluation of the completeness parameter was implemented by parameters such as the ratio of the length of the two data sets, the ratio of the number of constituent points of features, and the ratio of the number of features. From the above three criteria, the length ratio of the two data sets was selected as a suitable evaluation criterion by less pre-processing analysis such as creating the graph structure in the number of features criteria and simplifying the lines in the constituent points of features. However, the length criterion in topologically erroneous datasets strongly affects the evaluation results. At the end, after introducing and using the vector matching algorithm based on determining the geometric similarity of the features, the completeness parameter was calculated as 66.2. High rate of two quantities of omission and commission information of the VGI data set compared to the official data set of 33.8 and 47.8 in the current study due to the weakness of the matching algorithm in the two data sets, it is expected that with the improvement of this algorithm, the amount of identified omission and commission information will be reduced.}, Keywords = {Volunteered Geographic Information, Quality Assessment, Completeness, Object-based Methods, Unit-based Methods}, volume = {9}, Number = {4}, pages = {93-113}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-844-en.html}, eprint = {http://jgst.issgeac.ir/article-1-844-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {ZamingardRouzbahani, A. and Vafaeinejad, A. R.}, title = {Traffic Noise Mapping in Urban 3D Area by Using GIS and CORTN Model}, abstract ={Urban communities have been developing and they are being industrialized. These developments have some benefits for these communities; however, they have created some significant problems. One of these problems in this area is traffic and road congestion and following that, noise pollution on urban areas. These days, noise pollution is one the considerable problems that the residents of crowded places in urban communities are dealing with. Noise pollution not only decreases the quality of urban life, but also its mental and physical side effects are lasting for years. Therefore, considering this issue, attentions must be paid to this topic and conducting research is necessary. The results of these researches can contribute to offering solutions and analyses for planning for controlling the noise pollution on urban communities. In this study we aim to present a 3D model of an urban area in which the amount of noise pollution caused by traffic on the surfaces of buildings is specified in the form of color maps with noise scales. For this, at first, a comprehensive analysis of the proposed models in the area of predicting noise was done. The models were CORTN،FHWA،CNR،RLS90،SCM1. Each of These models has calculating relations and characteristics unique to them. After analyzing these models from calculation and structural aspects, we chose the CORTN model for the predicting the noise. This model was chosen as the final model in predicting the noise due to its simplicity and calculability of all of its parameters and also its ability for calculating the noise scales in different heights. Then, with creating an arranged network of the areas in the 3D example in the level of region and doing three stages of refining on them, the amount of noise in all areas was calculated. These three steps of refinery were done for decreasing the extra and unnecessary points in a way that the remaining areas have logical long and wide distance from each other and be consistent of the outer surfaces of the buildings. Because for predicting the noise we just need those areas that are on the outer surfaces of the buildings and we do not need to predict the noise in other areas. With doing these three stages of refinery, the number of sample areas was decreased from 896000 to 11456. This reduction in the number of areas did not influence the final accuracy. However, because of the significant reduction, it increased the speed of calculation and the saving in time. It is worthy to note that all calculations related to predicting noise were conducted by through formulas in Microsoft Excel. Finally, after the calculating and predicting the noise in all sample areas, we use Kirging interpolation model to create interpolation maps of areas in the ArcGIS software and these maps were merged with the surfaces of the buildings by Sketch UP software. Then, the created model was moved to ArcGIS and finally, the built model was presented in it. The results of this study show the distribution of noise pollution on different surfaces. Knowing this awareness can help us in better planning for choosing the kind of usage of different floors in a building. Because we cannot decrease the number of cars in the urban communities these days and we are dealing with increasing number of cars, therefore, we must conduct such researches in order to deal with this kind of pollution an use them in designing and constructing the buildings.}, Keywords = {Noise Pollution, Interpolation Model, Optimizatiom Methods, GIS, CORTN}, volume = {9}, Number = {4}, pages = {115-130}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-905-en.html}, eprint = {http://jgst.issgeac.ir/article-1-905-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Boroumand, F. and Alesheikh, A. A. and Farnaghi, M.}, title = {Measuring the Similarity of Trajectories Using Fuzzy Theory}, abstract ={In recent years, with the advancement of positioning systems, access to a large amount of movement data is provided. Among the methods of discovering knowledge from this type of data is to measure the similarity of trajectories resulting from the movement of objects. Similarity measurement has also been used in other data mining methods such as classification and clustering and is currently, an important and challenging topic for many researchers in the field of geospatial information systems. Although uncertainty is an inevitable issue in the field of geospatial information systems, so far little attention has been paid to this issue especially in the field of measuring the similarity of trajectories. One way to cope with the uncertainty in the observations and definitions of the problem, is to use fuzzy theory. In this study, two methods of sim1 and sim2 based on Longest Common Subsequence (LCSS) and Edit Distance on Real Sequence (EDR) methods, respectively, have been introduced to deal with uncertainty in measuring similarity of trajectories and improving their performance using fuzzy theory. The proposed methods use a fuzzy membership function based on the distance between the points of two trajectories to determine the degree of matching of every pair of points on two trajectories based on which the similarity of the two trajectories is calculated. In order to evaluate these two methods, two experiments have been performed on the real and synthetic trajectories of personal cars. Experimental results show that sim1 and sim2 are similar to LCSS and EDR in terms of sensitivity to noise, increasing and decreasing sampling rate and have better performance in terms of sensitivity to displacement. For example, the mean percentage change of similarity to distance variations for the four thresholds of 5, 10, 25, and 50 meters for LCSS is 0.02, 0.97, 0.66, and 0.23 but for sim1 and sim2 is 0.41 which is proportional to rate of changes in reference trajectory.}, Keywords = {Trajectory Similarity, Geospatial Information System, Data Mining, Uncertainty, Fuzzy Theory}, volume = {9}, Number = {4}, pages = {131-143}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-891-en.html}, eprint = {http://jgst.issgeac.ir/article-1-891-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Asgharzadeh, M. and Alesheikh, A. A. and Sadeghi-Niaraki, A. and Ebrahimian, Y. and Shirzad, R.}, title = {Predictive Risk Mapping of Leptospirosis for North of Iran Using Pseudo-absences Data}, abstract ={Leptospirosis is a common zoonosis disease with a high prevalence in the world and is recognized as an important public health drawback in both developing and developed countries owing to epidemics and increasing prevalence. Because of the high diversity of hosts that are capable of carrying the causative agent, this disease has an expansive geographical reach. Various environmental and social factors affect the spread and prevalence of the disease. The combination of epidemiology and Geospatial Information System plus using Ecological niche modeling provides the ability to identify areas at risk of disease, then predict the risk map of the disease for other regions by using relevant environment variables, and prevent and eventually eradicate the disease by conducting constructive activities such as increasing public awareness with education. In this study, using land use, environmental, and climate variables and taking advantage of the occurrences of the disease between 2009 and 2018, the risk level of Leptospirosis was modeled in three provinces of Gilan, Mazandaran, and Golestan based on ecological perspective. For modeling, highly correlated variables and also variables with high multicollinearity were identified and omitted. Because in ecological modeling regions to represent the absence of disease is required in addition to the presence and since these areas are not available, the second objective of this study was to evaluate the efficacy of different methods of generating pseudo-absence data in modeling leptospirosis. Finally, more accurate modeling of the prevalence of the disease in the northern provinces of the country can be obtained. Therefore, after selecting suitable variables for modeling, first, based on five methods (completely random generation of points in the study area, applying physical constraints with buffer at two radii of 5 and 10 km the generating points outside of designated buffer, applying environmental constraints by implementing two models of one-class support vector machine and BIOCLIM and generating points in unsuitable areas defined by these two models) pseudo-absence points representative of disease absence points in the study area were produced. Next, four models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine were deployed to produce the disease risk in the study area. BIOMOD2 package in the R programming language was applied for modeling. The results showed that applying physical constraints with buffers yields the most reliable performance in comparison to the other three methods. Finally, the constructed model that performed best in TSS Statistics (with values of 0.76, 0.87, 0.84, 0.82 for Models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine) was considered as the final output. Between all deployed models, Artificial Neural Network delivered the worst performance and had the most unstable results. Based on the risk-map of leptospirosis, central regions of Mazandaran and Gilan province, especially rural areas of Layl, Asalam, Eslam Abad, Chahar-deh, and Lafmejan have very high values of risk. Measures need to be made to reduce the high rate of Leptospirosis incidence in these regions. Furthermore, yearly precipitation was considered the most influential variable for the distribution of Leptospirosis.}, Keywords = {Leptospirosis, Geographic Information System, Disease Distribution Modelling, Pseudo-absence}, volume = {9}, Number = {4}, pages = {145-156}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-890-en.html}, eprint = {http://jgst.issgeac.ir/article-1-890-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Bayat, M. and Hoveidi, H.}, title = {Identification of suitable sites for landfill wastes of temporary settlement areas after the earthquake crisis using Particle swarm optimization method}, abstract ={Our country has experienced flood and earthquakes in most cities, due to its location in the Alpine-Himalayan mountain belt, with varying climates which they are so destructive. The earthquake is considered as a harmful natural hazard that affect human society around the world, and is extremely dangerous because it cannot be foreseen and its energy can be freed at any time, and is classified in the catastrophe. This disaster is quite probable in Tehran since it is surrounded by powerful faults. So, by considering the fact that it is the most crowded city in Iran with heavily populated areas, and its importance in terms of political and economic issues, there is a vital need for devising appropriate plans for the period of after earthquake. Waste landfill after a destructive earthquake is one of the most important environmental, health and humanitarian problems in the crisis response process. And because it is not predictable and its energy may be freed at any moment, it is very dangerous and placed in the classification of catastrophe.. Given that, after evacuating people from residential areas, the damaged people usually live in temporary accommodation, so there should be more places than the pre-crisis situation to land the waste. Given the temporary nature of these sites and the environmental impacts of these sites, it is necessary to introduce an optimal location. Given the temporary nature of these sites and the environmental impacts of these sites, it is necessary to introduce an optimal location. The purpose of this paper in the first step is finding suitable areas for the temporary accommodation after earthquake for the damaged people, and in the second step, Optimization of suitable places for waste landfill. The main innovation of this paper is the application of particle mass optimization (pso) algorithm in introducing temporary landfill sites. For this purpose, firstly, suitable places for temporary accommodation were determined by determining the relevant criteria, weighing them by the Fuzzy-OWA and the combining layers using weighted overlap method. Suitable landfill sites were selected according to the urban extension process. The results show that according to the three modes, OR, AND and WLC, on average, sites of 5 and 10 are suitable places for landfill. Also, the most suitable landfill sites are located west of Tehran. But due to the physical expansion of Tehran to the west and southwest in recent decades, the winds from the west and the presence of high and mountainous areas in the north of this city and the probability of further rainfall in these areas, in general, the areas in the south and Southeast of Tehran, will be more suitable for landfill, because by creating burial places in the west of Tehran, due to urban physical expansion, there are many problems, including environmental and economic problems. Therefore, sites 9, 10 and 11 located west of Tehran have been eliminated from the options. And among other sites, the most suitable site for landfill site is 5. And sites 1, 5 and 4, respectively, are prioritized for landfill.}, Keywords = {Temporary Settlement, Earthquake, Waste Landfill, Optimal Site Selection, Particle Mass Optimization Algorithm, Ordered Weighted Average}, volume = {9}, Number = {4}, pages = {157-168}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-851-en.html}, eprint = {http://jgst.issgeac.ir/article-1-851-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Sherafati, L. and Aghamohammadi, H. and Behzadi, S.}, title = {Modeling and Spatio-Temporal Analysis of the Distribution of O3 in Tehran City Based on Neural Network and Spatial Analysis in GIS Environment}, abstract ={Air pollution is one of the most problems that people are facing today in metropolitan areas. Suspended particulates, carbon monoxide, sulfur dioxide, ozone and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. The goal of this study is to propose a spatial approach for estimation and analyzing the spatial and temporal distribution of ozone based on GIS analysis and multi perceptron neural network. In the first step, by considering the accuracy of different interpolation methods, IDW method was selected as the best interpolation method for mapping the concentration of ozone in Tehran. according to the daily data of these pollutants, the daily, monthly, and annual mean concentrations maps were prepared for years 2015, 2016 and 2017. According to the results, it can be said that the highest concentrations of ozone are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. According to the data of ozone pollutants in year 2018, the accuracy of neural network for hot and cold days of year were about 68% and 77% respectively. Therefore, it can be said that the meteorological parameters of temperature, wind speed and direction, and precipitation are significantly related to the concentration of O3 pollutant.}, Keywords = {Spatial Analysis, Neural Network, GIS, O3}, volume = {9}, Number = {4}, pages = {169-180}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-900-en.html}, eprint = {http://jgst.issgeac.ir/article-1-900-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Babaee, S. S. and Mashhadi-Hossainali, M. and SamieEsfahany, S.}, title = {A Review of the Three-dimensional Field Displacement Retrieval Methods Using Interferometric Synthetic Aperture Radar Observations (InSAR) With Emphasis on the Precision of Each of these Methods}, abstract ={Interferometric Synthetic Aperture Radar (InSAR) technology provides a useful tool for quantitatively measuring the deformation of the earth, influenced by natural factors (earthquake, subsidence, and landslide) and human factors (construction of structures, drilling, and the overexploitation of underground water aquifers). In this context, time-series analysis of radar images allows the monitoring of long-term deformations and analysis of geodynamic phenomena. However, the radar interferometric technique is only capable of measuring the displacement along the satellite's line of sight (LOS), and one interferometric LOS observation is not capable of extracting a 3D displacement field. Thus, this will limit the potential of the InSAR technique to the study of many tectonic phenomena that require a comprehensive understanding of their three-dimensional displacement components. The purpose of this paper is to provide a comprehensive overview of the main methods of retrieval of the earth's 3D surface field using radar interferometric observations, including; recent advances in this field and the advantages and weaknesses of each of these methods. In fact, in this paper, the existing methods for recovering 3D surface displacement fields using radar interferometry measurements developed in recent decades are reviewed in detail. Several methods are used to exploit the potential of InSAR for 3D surface displacement determination. In general, these methods can be divided into three general categories. The first is the use of homogeneous data, including radar images of other satellites or the use of independent radar imaging geometries. In this category, we can mention ideas such as 1) Using the observation combination along the satellite LOS in at least three independent geometries (DInSAR) 2) Combining the observations along the LOS with the azimuth observations (azimuth offset, MAI) and 3) Overlapping between the burst (burst overlap interferometry) in the Sentinel satellite data. In the first batch methods, the accuracy of estimating 3D field components for different geometries is estimated. However, it has been observed that the least accuracy will be related to the north-south component retrieval. The second batch method is the use of heterogeneous data (independent geodetic observations such as GPS, leveling, gravity data, etc.), which through combining GPS displacement vectors or leveling with observations from radar interferometry (the so-called data fusion) tries to recover the real 3D dimensional field, And finally, the third set of methods includes previous studies and experiences of how to relocate the area or use assumptions about the relocation of the area, in which there are two methods; 1) Ignoring one or two displacement component (if the displacement mechanism is known), which provides an analytical form for measuring the error in retrieving the other components and evaluates with similar data. 2) Considering the hypothetical models for reshaping or combining geophysical models with radar interferometry data. Each of the methods mentioned above is used for retrieving the 3D displacement field and have their strengths and weaknesses, Which are discussed in detail in this article. Finally, we hope to provide useful guidance for choosing a suitable method to resolve the challenging issues of extracting a 3D surface displacement field.}, Keywords = {Radar Interferometric, Horizontal and Vertical Displacement, Line of Sight (LOS), Three-dimensional Displacement Components}, volume = {9}, Number = {4}, pages = {181-203}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-867-en.html}, eprint = {http://jgst.issgeac.ir/article-1-867-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Saadatzadeh, E. and Chehreghan, A. R. and Aliabbaspour, R.}, title = {An Evaluation of Infrastructure-free and Infrastructure-based Indoor Positioning Methods with the Focus on Pedestrian Dead Reckoning}, abstract ={The expansion of location-based services (LBS) and their applications has led to a growing interest in localization, which can be done on the smartphone platform. Various positioning techniques can be used for indoor or outdoor positioning. Indoor positioning systems have been one of the most challenging technologies in location-based services over the past decade. Considering the increase of people activities inside buildings such as offices, hospitals, and large stores, determining the position and guidance of people inside these buildings is one of the most urgent and important issues to be discussed and challenged in the area of Location-based Services (LBS). There are various ways to determine the position inside a building. The method(s) used to determine the position in an indoor environment depends on several factors such as cost, accuracy, independence of, or dependence on the infrastructure, security, and system scalability. This study focuses on the infrastructure requirements necessary to determine the position of individuals thorough a comprehensive study of previous studies. Moreover, focusing on the Pedestrian Dead Reckoning positioning method using smartphones as an infrastructure-free method, several effective aspects of the accuracy and positioning process are examined. The effective measures examined include the use of a variety of noise filtering, combined filters (Particle filter, Kalman filter), the criterion of the of sensor data classification algorithm, the criterion of the initial point determination, the use of landmarks as checkpoints and plot maps for setting the estimated position, the detection criteria and estimation of the length of the step, and the user direction estimation criteria. The particle filter has good accuracy in small-scale areas, but in large-scale areas, it is out of date and has problems due to the limited source of the smartphone. In studies, Kalman filter has been used to integrate the information of different sensors, some of which have reached the desired accuracy according to the state model and the measurement model. Given that the generalized Kalman filter has a simple formula for nonlinear estimation, the linearization of the positioning problem causes an error in the Jacobi Matrix model and reduces the accuracy of the estimate, which negatively affects the cost of calculations and system timeliness. Step length varies from person to person. In fact, there should be a variable associated with pedestrians in the step estimation model. Also, a person's walking rate during a walk is not constant. Accordingly, assuming a constant value of step length for users causes an error during positioning and a large drift at the end of the path. Determining the heading is one of the most challenging parts of the PDR system because the heading error leads to a quick increase in the positioning error. It is difficult to determine the reliable heading in the environments with high magnetic disturbances. Another problem is that the heading of the smartphone may vary with the heading of the pedestrian movement. Therefore, two main tasks must be performed before implementing indoor positioning. One of them is to determine the heading of the smartphone. Another is to infer the offset heading between the smartphone and the pedestrian movement. Therefore, determining the state of the smartphone is necessary for specifying the heading of the pedestrian movement.  Finally, the advantages and disadvantages of each of the infrastructure-based and infrastructure-free methods are compared and evaluated. Also, the research uses algorithms such as Naive Baye, MLP, SVM, DT and KNN to classify the type of user movement and phone holding mode.}, Keywords = {Indoor Positioning, Infrastructure-free and Infrastructure-based Positioning Methods, Pedestrian Dead Reckoning, Smartphone Sensors}, volume = {9}, Number = {4}, pages = {205-233}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-956-en.html}, eprint = {http://jgst.issgeac.ir/article-1-956-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Rezaeian, M. and AliAbbaspour, R. and Bahramian, Z.}, title = {A Review of Spatial Factor Modeling Techniques in Recommending Point of Interest Using Location-based Social Network Information}, abstract ={The rapid growth of mobile phone technology and its combination with various technologies like GPS has added location context to social networks and has led to the formation of location-based social networks. In social networking sites, recommender systems are used to recommend points of interest (POIs) to users. Traditional recommender systems, such as film and book recommendations, have a long history. However, due to the existence of the location component and the physical connection of users with the outside world in social networking sites, several special features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of location-based social network data, spatial factor plays an important role in improving recommendations. Because people's desire to visit places is greatly influenced by the distance between the person and the place. Also, the distribution of POIs in the region changes the pattern of user visits. In the first part of this study, we discuss challenges which social networking sites may face by comparing location-based recommender systems with traditional recommender systems. In the following, we mention some important contexts and factors in POI recommendation. Spatial factor, social relations, different types of contents, different categories, sequential pattern, and time factor are contexts which are commonly used in POI recommendation. Next, we mention different types of location-based recommender systems: the fused model and the joint model. In the fused model we model user’s preferences and other additional contexts individually and after that, we combine their results with collaborative filtering. In a joint model, all contexts are learned Simultaneously.  In the next part, we discuss methods for extracting spatial context in location-based recommender systems. There are three major ways of modeling spatial data: independent, dependent, and restrictive models.  In independent modeling, we model spatial factor independently without considering the user’s preferences and other contexts. Here we discuss four basic independent models in detail: power law, Gaussian distribution, Kernel Density Estimation, and distance-based models. The power law is a relationship between two quantities in which a relative change in one quantity causes a change in another quantity, and this change is independent of the initial values ​​of the two quantities. This rule is used for modeling spatial data in recommender systems. Changes in many natural quantities around a constant value follow the Gaussian distribution, and this has led to its use to model spatial factors. Kernel density estimation is a non-parametric method for estimating the probability density function of a random variable. To recommend personalized items this method can be very useful because we could model spatial data of every user individually. distance-based methods model spatial factor by considering the distance between users and items or items with each other.  At dependent modeling spatial context is learned with other contexts Simultaneously. For this, we determine four popular methods: matrix factorization, probability-based models, artificial intelligence, and combined models. These methods are general algorithms for recommending items in recommender systems and spatial factor is just one of their components. Restricted models filter recommendations by considering spatial constraints.  At the end of the article, we summarize the various features of the proposed methods and mention their advantages and disadvantages.rapid growth of mobile manufacturing technologies and its combination with various technologies have led to the addition of location dimension to social networks and the formation of location-based social networks. Recommender systems are used on location-based social networks to recommend points of interest to users. Traditional recommender systems such as movies and book recommendations have a long history. However, due to the locational component and physical connection of users with the outside world in location-based social networks, several specific features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of the location-based social network’s data, location factor plays an important role in improving recommendations. Because people's desire to visit places is largely influenced by the distance between the person and the place. The distribution of attractive places in the area also changes the pattern of user visits. In this study, we first discuss the challenges that location-based social networks face by comparing them with traditional recommender systems. Next, the factors that influence location recommendation in location-based recommender systems are discussed in detail. Finally, a variety of location modeling methods, which is one of the most important factors in recommending attractive locations to users using location-based social network data, are discussed.}, Keywords = {Point of Interest Recommendation, Spatial Factor Modeling Techniques, Location-based Social Network, Recommender Systems}, volume = {9}, Number = {4}, pages = {235-255}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-918-en.html}, eprint = {http://jgst.issgeac.ir/article-1-918-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ramezani, A. and Khosravifard, K.}, title = {Development of a Spatial Model for Locating Optimal Areas of Sustainable Physical Development Using Fuzzy Logic (Case Study: Hamadan City)}, abstract ={Today, physical development and population growth in Iranian cities, like other developing countries, is on the rise. One of the main problems in the urban area is the lack of attention to the influential parameters in the sustainable urban development.  Various factors, such as natural phenomena, play a role in the urban development, and the effective parameters must be considered for locating the suitable areas for sustainable physical development. Otherwise, there will be a kind of heterogeneity and disorder in the physical development of the city and the sustainable development will not be achieved. The most important innovation of this research is the combination of spatial information system and fuzzy logic to determine the optimal range of the physical development of Hamedan city with a sustainable development approach. Since the determination of the optimal range of development has not yet taken place, and several factors have been involved, and uncertainty in development rang prediction, using fuzzy logic can be helpful.  In this study, the effective parameters are including faults, slopes, roads, rivers, and distance from the city are considered. Then fuzzy membership functions defined and applied spatial data using   ArcGIS software.  The Sugeno fuzzy system is used to determine best location of the urban physical development. In this system the fuzzy rule of a descriptive phrase with linguistic values, ​​becomes a simple relation and the output of the system is no fuzzy. Also, in this system, there is no need to apply the operator on the system and deactivate it. One of the advantages of Sugeno system is that its output can be used in most cases and there is no need to change it. In this system, the input of the system is applied to the rules and then multiplied by the weight of each rule and the result is divided by the sum of the values ​​of the rules. Due to population growth and the growing trend of physical development in developing countries, including Iran, it is necessary to rely on scientific tools and by examining various factors to advance this development towards sustainable physical development. Statistical studies have shown that the population of Hamadan has been increased over the past. It should be noted while planning for the sustainable physical development of cities, the factors of physical development must be examined and acted based them. For analyzing effective parameters of sustainable development Geo-Spatial Information System was used.  Due to uncertainty existence in urbanization, fuzzy logic was used as a suitable tool to model these parameters. Finally, the results were presented by combining different parameters. years. The results show that the best area for sustainable physical development of this city is the northern and northwestern parts of the city. Also, if the importance of the beauty parameter of nature decreases, the northeast of the city will be suitable for development. But in the none of the studied cases the south of the city is not suitable for development.}, Keywords = {Urban Development, Spatial Information System, Fuzzy Logic, Sustainable Development}, volume = {9}, Number = {4}, pages = {257-267}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-874-en.html}, eprint = {http://jgst.issgeac.ir/article-1-874-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} }