@article{ 
author = {Shaeri, M. and Abaspour, R. A.},  
title = {Design and Development of an Information Collection System for Medium Voltage Distribution Network based on a Location-based Data Model}, 
abstract ={The continuous growth of cities, their populations, industrial and agricultural centers have led to an ever-increasing rise in subscribers’ demand, and power distribution networks as the last in the supply chain, alongside other sectors, should be responsive to the requirements of the community in various regions of the country. On the other hand, it’s inevitable to have a comprehensive understanding of the condition of the equipment and necessary actions which should be made in case of unexpected incidents in power distribution networks. Hence, across the country, several plans are established for the collection of information on the equipment of the power distribution network. The basic problem in this regard is the traditional way of collecting information, which does not allow logical control and the processing of the intermediate information and extraction of errors in all stages of data surveying. Consequently, numerous researches have been conducted with the purpose of the design and developing the appropriate data model considering the abovementioned issues. Many of the introduced models are suffering the absence of spatial aspect which is the primary goal of information collection plans and mainly focus on the complicated logics of power distribution networks. In contrast, other researchers take the spatial component of the model into consideration and neglect basic power distribution network concepts. Taking the precedent described models, it’s obvious that both types of models will fail in achieving the target defined for power distribution networks’ information collection plans. Therefore, the design and development of a location-based model with satisfactory simplicity resulting in accelerating the information collection procedure and adequate level of power distribution network concepts’ details is significantly indispensable. In this paper, an innovative attitude through faster collecting and controlling of information is addressed with state-of-the-art technologies in spatial information systems and a location-based data model is introduced to solve the before mentioned issues. In the first step, a location-based data model is designed to address the existing issues. Four main equipment whose location is of high importance are medium voltage poles, high voltage transformers, ground distribution posts, and ground medium voltage lines. Other equipment is mounted on the four location-based equipment and perceiving their relative position is ample for rebuilding the position of whole network equipment. In this way, we have improved the speed of the location assignment process. Afterward, for all devices, properties such as parent devices, connected devices, quantity, and textual attributes are defined. The defined properties are the keystone of the data model structure. In the next step, the mobile application is developed for data collection thanks to the Cordova platform allowing developers to produce their application in assorted mobile operating systems with a single same code. In addition, the collecting data application improves the positioning accuracy of equipment by exploiting aerial maps alongside the GPS sensor. The final output of the application is a single SQLite file. Eventually, the desktop application is implemented with the Electron platform, a Cordova counterpart in desktop operating systems, for checking logical errors of collected data due to users’ faults by analyzing the location and relation of equipment, as well as matching and joining of equivalent equipment from different data sets for data preparation in importing phase. At the evaluation stage, 450 km of medium voltage power distribution network, comprising of 37295 devices collectively, was processed and 92% of the network equipment was matched and imported. Best matched equipment includes medium voltage poles 99%, medium voltage isolators 98%, and overhead medium voltage lines 90%.},  
Keywords = {Medium Voltage Network, Location-based Data Model, System},
volume = {10},
Number = {3}, 
pages = {1-12}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-861-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-861-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Faraji, M. and Nadi, S. and Shojaei, D.},  
title = {Spatial-Temporal Prediction of PM2.5 Pollutants Using Deep Recurrent Networks: A Case Study of Tehran}, 
abstract ={In recent years, air pollution has become one of the most important environmental challenges in large and industrial cities such as Tehran. High concentration of particulate matter with a diameter of less than 2.5 μm (PM2.5), which is known as the main cause of pollution in Tehran, is associated with irreversible effects on human health. Providing spatial-temporal model with high accuracy and speed for forecasting, is an effective way to protect public health against the increase of harmful air pollutants. The rapid growth of computing technologies and the availability of air quality data have provided researchers with the opportunity to provide sophisticated models in the context of machine learning, especially in deep learning to predict the concentrations of various air pollutants. In this study, with the aim of predicting PM2.5 concentrations at different time intervals, a new spatio-temporal deep learning model based on gated recurrent units (GRU) is presented which maintains and extracts temporal and spatial dependencies in the time series of air pollution datasets. The proposed model has been compared with support vector machine regression (SVR) and long-term memory (LSTM) methods as competitive approaches. The data used in this study include the hourly concentration of PM2.5 and meteorological parameters recorded by 13 air pollution monitoring stations and 3 synoptic meteorological stations in Tehran in the period of December, 2016 to February, 2019, respectively. The model presented in this paper with the RMSE of 7.97 μg/m3 and MAE of 5.35 μg/m3 has the best result for predicting air contamination compared to other methods. This model can determine 80% (R2=80) of PM2.5 concentration changes and predict contamination level. The proposed model also proves that it can be used effectively to predict and control air pollution by extracting temporal properties, simultaneous forecasting for all stations and considering spatial correlations.},  
Keywords = {Air Pollution, Deep Learning, Spatio-temporal Prediction, PM2.5, Machin Learning},
volume = {10},
Number = {3}, 
pages = {13-26}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-966-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-966-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Karimzadeh, S.},  
title = {Identifying Collapsed Buildings after Sarpol-e Zahab Earthquake Using Multisensor Analysis and Machine Learning}, 
abstract ={Earthquakes and their consequences should be studied in detail in order to reduce the number of casualties in future events. From the beginning of the twenty first century until now more than 800000 deaths were reported, in which most of the casualties are located in Alp-Himalayan seismic belt. Bam earthquake in 2003 in central Iran, with more than 26000 casualties, Indian Ocean earthquake in 2004, with approximately 200000 casualties, Sichuan earthquake in 2008 in China with more than 96000 casualties, and Haiti earthquake in 2010 in Haiti with approximately 321000 casualties are only a few given examples that how devastating the earthquakes can be. Instant deaths right after a strong earthquake is primarily because of physical contact of rubbles material with exposed people, but the second phase of casualties emerge due to injuries, suffocation of trapped people among the rubbles and wasted materials, and collateral hazards such as fire. Although the instant deaths look inevitable, second phase casualties can be decreased by addressing rapid disaster response based on recent remote sensing earth observation systems to bring the quality of search and rescue teams to an actionable level, especially for night-time earthquakes. In SAR remote sensing imagery, addressing of seismic damage states initiated with simple indices such as difference and correlation of SAR backscatters of pre- and post-event images, difference of coherence value of interferometric phase analysis, and their combination. Furthermore, regression analysis of SAR backscattering of pre- and post-event images together with seismic intensity were also applied for deeper understanding of the earthquake damages. In the recent developments of earthquake damage assessment, combination of multitemporal dual-polarized SAR data, combination of multitemporal ascending-descending SAR data and only post-event SAR data are common methods to decrease the level of uncertainty. In the optical remote sensing, damage assessment was initiated by visual comparison of pre- and post-event images. However it is possible to apply methodologies based on only post-event images if lower accuracy is needed. Therefore, visual interpretation of optical images, rather than automated change detection, is widely used in practice for building damage detection. Saito et al. (2004) visually interpreted collapsed buildings using three IKONOS images taken before and after the Gujarat earthquake, and confirmed the quality of the results by ground survey data. Further, Saito and Spence (2005) compared the visual interpretation results from only post-event QuickBird images with those from pre- and post-event images, and revealed that the building damage tended to be underestimated when only post-event images were available. Adams et al. (2005) used a visualization system integrated pre- and post-event QuickBird imagery to direct rescuers to the hardest hit areas and support efficient route planning and progress monitoring in the emergency response phase of the Bam earthquake. By comparing the pre- and post-event QuickBird imagery visually, Yamazaki et al. (2005) classified the damaged buildings caused by the Bam earthquake into four damage grades (EMS98). Comparing the results to field survey data revealed that the pre-event imagery was more helpful in detecting lower damage grades through visual interpretation. Here various machine learning based techniques for performance understanding of the classifiers in an urban scale is presented. This study covers a comprehensive seismic damage assessment of Sarpol-e Zahab town in western Iran which was affected by an earthquake M 7.3 on 12 November, 2017. The damage concept is evaluated using both synthetic aperture radar (SAR) and optical images. Two pre-event and one post-event dual-polarized high resolution SAR images of ALOS-2 satellite, and one pre-event and one post-event very high resolution optical images of WorldView-2 satellite (4 bands) are contributed in the comprehensive seismic damage assessment. In SAR dataset, twenty-four influential parameters are extracted from interferometric phase correlation (differential coherence), differential intensity, and differential texture analysis of HH and HV channels, whereas in optical dataset, twenty influential parameters are derived from differential texture analysis of red, green, blue and infrared (IR) bands. For the derived parameters of each dataset, principal component analysis (PCA) and machine learning based algorithms (i.e. random forests, support vector machine, naive Bayes, k-nearest neighbors and regression tree) are carried out in order to extract the damage maps and their related accuracy with respect to the calibration data which is acquired from United Nations Institute for Training and Research (UNITAR).},  
Keywords = {Synthetic Aperture Radar, Damage Assessment, Machine Learning, Texture Analysis},
volume = {10},
Number = {3}, 
pages = {27-39}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-904-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-904-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Mohammadi, M. and TabibMahmoudi, F.},  
title = {Vehicle Recognition Based on Object Based Analysis of Airborne Remote Sensing Images}, 
abstract ={Introduction: Vehicle detection and counting is an important issue for many applications such as remote monitoring, vehicle tracking for security purposes, traffic management, rescue tasks, parking capacity analysis and metropolitan planning. Vehicle monitoring is also an important part of traffic information, crash control, vehicle flow statistics, road network planning and parking position estimation. Remote sensing images are widely used to monitor vehicles, due to the ability of sensors in providing a complete coverage of the area of interest. Compared with satellite imagery, aerial imagery is usually more considered for vehicle detection and traffic monitoring purposes due to the higher spatial resolution. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects, such as air conditioning in buildings, trash cans and road signs in high resolution images. Lots of researches have been carried out on vehicle recognition in aerial images over the past years. These works can be categorized into the two main groups; shallow learning based methods and deep learning based methods. Most of the researches proposed in the deep learning category use Convolution Neural Network (CNN) for automatic features extraction. Although local convolution neural networks have performed well in object recognition from images, their performance in aerial imagery is limited due to the small sizes and orientation of vehicles, the complex background in urban areas, and difficulties in rapid detection due to large covering area. The general strategy that is applied in shallow learning based methods relies on hand crafted features extraction followed by a classifier or cascade of classifiers. Method: In this paper, a shallow learning based vehicle recognition algorithm is proposed for aerial imagery. This method uses the advantages of object based image analysis and the image pyramid. The proposed automatic vehicle recognition algorithm is a decision fusion strategy between the initial vehicle candidates and land use/cover classification map to modify vehicle recognition results. The initial vehicle candidates are recognized by structural object classification based on image pyramid. The proposed algorithm for initial vehicle candidates generation is composed of four main steps; 1) generating image pyramid, 2) performing image segmentation on the pyramid layer, 3) structural features measurement on the segmented image objects of pyramid layer and 4) performing knowledge based classification of the image segments into the vehicle and no-vehicle classes to produce a binary map containing only the initial candidates of vehicles. The land use/cover classification map is also generated in an object based image analysis procedure. In the final step of the proposed automatic vehicle recognition in this paper, a decision fusion algorithm is performed between initial vehicle candidates and the generated land use/cover classification map. In this procedure, the recognized initial vehicle candidates from pyramid layer should be transferred to the original image resolution by performing inverse pyramid transformation. Then, considering the meaningful neighboring relationships between vehicles and other defined object classes, the final and modified vehicle regions are recognized. Results: The ability of the proposed vehicle recognition method in this study is evaluated based on Ultracam aerial imagery with spatial resolution of 11 cm and four spectral bands in visible and NIR that is taken from an urban area in southwestern Russia. The extent of this study area is about 5900 to 9100 pixels. The obtained results showed the vehicle recognition accuracy for about 80%. Moreover, %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the final classification map from the proposed decision fusion algorithm. The decision fusion algorithm can decrease false positive pixels in the vehicle recognition results by performing reasoning rules based on the relationships between vehicles and other objects such as buildings and roads.},  
Keywords = {Vehicle Recognition, Land use/cover Classification, Pyramid Layer, Object Based Image Analysis},
volume = {10},
Number = {3}, 
pages = {41-51}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-945-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-945-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Sharifi, M. A. and KarimiNezhad, M. M. and AmiriSimkooei, A. R.},  
title = {Initial Orbit Determination of the Earth Orbiters Using a Single Ground Optical Tracking Station Based on the Tikhonov Regularized Total Least Squares Estimation}, 
abstract ={Increasing demand on the launch of the Earth orbiters with a variety of applications makes the problem of the initial orbit determination problem more interesting. The problem is to determine the Keplerian orbital elements of any orbiters using minimum number of observations.  Observing the orbiters in their initial phase of orbital launch using the ground optical tracking stations is one of the most reliable and frequently used methods for the problem solution. Slant distance, horizontal and vertical angles of satellite with respect to the local north and zenith are the observed quantities in the optical tracking systems. Short-arc of the observations in particular for the Low Earth Orbiters (LEO) and modeling the initial orbit determination as a two-body problem and ignoring perturbing forces are the main challenging issues in orbital mechanics. Neglecting the perturbation force contribution in the mathematical models of the initial orbit determination sets up of the observation equations with some errors or the so called Error In Variable (EIV) model. Moreover, fitting an ellipse to the observed three dimension position time series of the orbiter and determination of the Kepler elements is an ill-posed problem. It is due to fact of fitting an ellipse to a few closely distributed data points along the orbit in three dimensional space. Therefore, one has to implement the method of Total Least Squares (TLS) with an appropriate regularization technique for the orbital parameter estimation. Different regularization techniques have been already introduced for solution of ill-posed problems. Herein, Tikhonov regularization method with the aim of minimization of bias term along with the error in measurements is applied and the orbital elements are estimated. Implementation of regularization method significantly improves the results and in particular the LEO Kepler elements. Numerically, the proposed method is implemented on the estimation of the parameters with different orbital geometry type; including the LEO and Medium Earth Orbiter (MEO) satellite in the polar and non-polar orbits. In all cases, the orbital parameters and their variances are estimated and statistically tested. Moreover, relative errors of the estimated parameters and their meaningfulness are checked in different scenarios.     Theoretically, the problem of orbital parameter estimation is a nonlinear problem by its nature. We are implemented iterative gradient-based solution and therefore linearization of the nonlinear equations is required. For quarantined convergence of the linearized model, initial value of the unknowns with acceptable accuracy is needed. The classical methods, e.g., Gauss, Gibbs and Lambert method of the initial orbit determination problem provide an approximate solution with enough accuracy for initialization of the estimation problem. The Picard condition as an indicator of ill-posedness on the inverse problem is used to demonstrate in the orbital parameters estimation. The L-curve method is implemented to get the solution with minimum bias value. The method of Ordinary Least Squares (OLS) is simultaneously implemented on the problem to show how TLS can efficiently be used.     },  
Keywords = {Initial Orbit Determination, Total Least Squares, Tikhonov Regularization, Kepler Elements, LEO Satellites},
volume = {10},
Number = {3}, 
pages = {53-67}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-947-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-947-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Ebrahimi, E. and Karimi, M. and Pilehforooshha, P.},  
title = {Rural Land Use Allocation Using Genetic Algorithm}, 
abstract ={Ruralization is a special form of peoplechr('39')s life and plays an important role in the processes of economic, social and political development. In this regard, the rural master plan is carried out in order to provide the ground for development and development and with the aim of appropriate and optimal allocation of rural land uses for sustainable development. However, the lack of optimal location of land uses is one of the weaknesses of these plans. This issue causes lack of proper access to land uses, incompatibility of a land use with adjacent land uses and as a result does not provide a suitable platform for village growth. In order to solve this problem, the purpose of this study is the optimal allocation of rural land by genetic algorithm as a suggested resource to help rural master plan consultants. To achieve this, restrictions on data were first applied, including river and poultry. Then, using four criteria including neighborhood (i.e., the integration of compatibility, dependence and centralization), accessibility, physical potential and resistance to change, and also considering the area specified in the master plan as demand for land uses, genetic algorithm is implemented in vector structure and rural land uses were allocated. It is worth mentioning that in this research, optimization is performed as a single objective problem and the objective function is considered as maximizing the weighted sum of defined criteria. Also, the considered land uses in this research include official, residential, green space and commercial land uses. The proposed model was implemented in Nematabad village using the user map of 1395, in order to produce the user map of 1396. In order to achieve the proposed land use map, first the weight of the model criteria in five different modes was changed and the model was validated in each mode using the calculation of kappa coefficient and overall accuracy. According to the results, the third case with a total accuracy of 71% had the highest total accuracy and, therefore, the weights assigned to the criteria in this case were used to prepare the final land use map. According to the proposed land use allocation map, it is clear that most of the commercial space is concentrated in one area. This is due to the higher weight of the centralization sub-criterion than other sub-criteria in calculating the neighborhood criterion. Based on the centralization parameter, the tendency to create a type of land use in the vicinity of the same land use is more and is done at a lower cost. Also, due to the compatibility of residential and green space land use with agricultural land use, these land uses are allocated in the neighborhood of each other. In addition, the results showed that neighborhood criteria and accessibility are the most important factors in the rural master plan. In future research, it is suggested that other optimization algorithms such as ant colony, and particle swarm optimization be used to optimally allocate rural land use and compare the results with the genetic algorithm. In addition, since this study uses four residential, green, official and commercial land uses in the allocation phase, it is suggested that other land uses such as agriculture be used in the allocation phase in accordance with the demand of that village.},  
Keywords = {Land use, Allocation, Genetic Algorithm, Optimization, Rural Planning},
volume = {10},
Number = {3}, 
pages = {69-86}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-936-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-936-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Abdali, S. and GhamaryAsl, M.},  
title = {Photosynthesis trend in terrestrial biosphere using MODIS GPP time series data during 2000-2015}, 
abstract ={Changing trends in ecosystem photosynthesis can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (fPAR) and Gross Primary Production (GPP). However, the estimation of trends from NDVI, fPAR and GPP time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. The global terrestrial GPP described as the total amount of carbon dioxide assimilated to the terrestrial biosphere by vegetation in photosynthesis. In other words, GPP is an essential flux of the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. Therefore, GPP plays a key role in the global and terrestrial carbon cycle. The utilized data in this research are NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Gross Primary Production and the Climatic Research Unit (CRU) meteorological data station. The MODIS photosynthesis model is based on the light use efficiency logic for calculating GPP. In this research, GPP dataset based on satellite observations and meteorological data has been used to estimate photosynthesis trend at a spatial resolution of 0.5-degree grid cell in terrestrial ecosystems from 2000 to 2015. Satellite remote sensing can provide continuous, repetitive, and consistent observations of dynamic changes in terrestrial ecosystem structure and function over large areas; it has become a more and more important tool for monitoring land surface properties.The objective of this research is to assess the trends of GPP using Mann-Kendall proxies at 90% confidence level and identify their key driving factors. This test enables the investigation of long-term GPP tendencies, without assuming that a given dataset follows a normal distribution. The Mann-Kendall test could apply to annual, seasonal, and monthly time series data. Generally, time series can be decomposed in a trend, seasonal, and remainder component. In time-series data, seasonality is the existence of variations that happen at particular regular intervals less than a year. Seasonal fluctuations may be caused by various causes, such as weather and consists of periodic, repetitive, and generally regular and predictable patterns in the time series. Seasonal fluctuation is an average that can be used to compare an actual observation relative to what it would be if there were no seasonal variation. After that, the spatial distribution of the linear regression of the GPP and meteorological data (temperature and precipitation) was calculated for each grid cell in the terrestrial biosphere for 2000~2015. Linear regression analyses are models that involve one independent variable (e.g. temperature or precipitation) and one dependent variable (GPP). Although earlier studies were carried out at the global or regional scales, these results cannot be easily matched with this investigation.  According to the results, despite the GPP fluctuations, the dominant trend, waiving the disturbance processes, is no-trend. The positive trends can be found in the southern parts of Africa, tropical regions in Asia and America which show increasing trends in GPP. The spatial patterns of the climatic controls on the annual variability of GPP is consistent with previous studies. The results showed that, in the high latitudes, temperature is clearly the dominant and limiting driver on GPP/photosynthesis. Stronger correlations between GPP and temperature than precipitation were observed.},  
Keywords = {Gross Primary Production, Spatio-temporal Trend, Photosynthesis, Terrestrial Biosphere, MODIS-GPP},
volume = {10},
Number = {3}, 
pages = {87-97}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-921-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-921-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Habibpour, F. and Feizizadeh, B. and Jabarzadeh, Y.},  
title = {GIS Spatial and Network Analysis Applied for Bookstores Geomarketing}, 
abstract ={Geo-marketing is a tool that uses geographic, or location-based, information to help companies put together marketing strategies and campaigns. Using digital mapping to organize and display data enables marketers to analyze data by region or a particular physical location. The chain stores are composed of several jointly owned and centralized retail outlets which coordinates their activities under a centralized organization. In geo-marketing, customer data from online transactions, mobile devices and other sources is stored in company databases. This information is applied to digital maps, for example, a zip-code map or a street map. Analysis of geo-marketing data helps marketers determine where their customers are, link data to a digital map to understand where their customers are in a geographical context, locate something on a digital map, create summary information for specific locations and choose customers in particular areas. Geo-marketing can also help marketers select customers similar to a specific type in the rest of the country or address problems regarding the location of a new office or store. Geo-marketing can be used for choosing a website for a new business or branch, determining key locations for advertising, displaying website content that is distinct to a user’s origin and offering online advertising based on a user’s location. Other applications include showing how a customer segment might be distributed in a particular. Geomarketing is a new way of knowledge-based marketing, which is supported by digital maps and specialized GIS software. Knowledge-based marketing use packaged information such as marketing information systems, such as model building, data mining, etc, in order to determine customer profiles, deviation analysis, and trend analysis.   Location Intelligence is a technical way to organize spatial data with business and human data in a geographically correct way in order to reveal hidden relationships that may lead to benefit for a business and/or to avoid spatially wrong located investments. It is combined with Business Intelligence (BI) in order to analyze and organize a vast amount of data and show the influence of geography on behaviors, activities and processes. Considering the given definitions it is clear that Geomarketing is a tool for either commencing or expanding a company and more or less, location is a key factor for geomarketing. Geographical locations together with demographic data are used in geomarketing analysis to study the routing plan, territorial planning and site selection.  Remote Sensing, GIS, GPS and virtual globes like Google Earth and World Wind of NASA form the four basic tools of geospatial technology. This technology is the spearhead of geospatial research in a) the connection between technology and thinking, b) training and c) professional upgrade.  All of the above tools are essential for the improvement of a business because they are real time data, they can collect, visualize and analyze their client’s assets in real time in combination with the real world of a satellite image or any other airborn imagery (i.e. image from a drone) and the process of the data in real time. This allows an almost instant updating of the maps used by the business. This can be done when the business uses a web mapping software in order to update their database. All web mapping software are on the cloud and give the opportunity to be used from any place any time by any employee of the company who has the right to do so. Also, the database is on the cloud and can be retrieved accordingly.   The purpose of this study is to improve the performance of chain stores in the 2 sub-region of Tabriz. For this goal, the location-based marketing was evaluated using network analysis as well fuzzy network analysis process. In order to apply the GIS based network and spatial analysis five major hypermarkets including Kourosh A, B, Refah, Janbo and boo were selected to be analyzed. We employed the new service area model to assess the accessibility of markets and their serveries area. The best service intervals with transit usage were identified in 3 minutes accessibility. This 3-minute range was identified as the most appropriate range of services using literature review and research background. To this end, three socio-economic, neighborhood and transportation criteria were applied with the relevant sub-criteria. Based on the ANP model, the Super Decision software was employed to derive the criteria weights; among the selected criteria. The socioeconomic criterion with the weight of 0.40193 and its respective sub-criteria (e.g. population density) were identified as the most influential factors in the geo marketing. Results indicated that parts of the Elgoli, Valiasr Jonubi, Parvaz, Elahi Parast as well as Part of Zafaraniyeh town together with 29 Bahman are classified to be in highly suitable area for marketing. Results also indicated that   the Janbo store is well located spatially and has a chance to build up the successful business. Results of this research are great of important for developing a GIS by means of bridging GIS and marketing and presenting new approach for GIScience.},  
Keywords = {Bookstores, Spatial and Network Analysis, Location-based Marketing, Super Decisions},
volume = {10},
Number = {3}, 
pages = {99-109}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-903-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-903-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Malek, M. R. and Asadi, N.},  
title = {Designing and Implementing a User Guide System in Indoor Spaces Using Context-aware Augmented Reality}, 
abstract ={In location-based services, increasing the user's interaction with the surrounding environment can increase their knowledge of that environment. Combining these services with Augmented Reality technology is one of the ways to increase this interaction. Augmented Reality combines virtual elements such as textual information, graphics, etc. with the real world and displays various objects from the real world with their corresponding virtual information to the user. However, using this technology in location-based services can cause problems. For example, by increasing the volume of textual and graphical information from the surrounding environment, displaying this information on the mobile device's screen with limited sizes, causes illegibility and reduces the usefulness of the information. Another problem with using Augmented Reality is the uniformity of the displayed information. That means, by changing the user's environmental conditions the information may change, but the displayed information through a non-context-aware system remains the same and does not change dynamically. In this research to overcome the problems mentioned above, a combination of Augmented Reality technology and Context-awareness has been used. Context-awareness considers the user's environment and its changes and modifies the system's behavior accordingly. To modeling the proposed system, first, we identified the effective contexts. We utilized four properties as context to guides the user in an indoor space; the distance between the user and possible target locations, rotation of the mobile device, the time that the user is using the application, and resolution. We used the Bounding Box concept to infer the resolution context. We then collected the required data to calculate the mentioned contexts. To find the user's position and calculating distance context, we used the Pedestrian Dead Reckoning (PDR) method. This method has less dependency on the environment and its infrastructures rather than other positioning methods like positioning using Wi-Fi and Bluetooth Low Energy (BLE) sensors. PDR uses the smartphone's IMU sensors to finding the user's orientation and detecting his/her steps. In this research, we used Accelerometer, Gyroscope, and Magnetometer sensors. Magnetometer sensors are mostly affected by surrounding iron objects. So we calibrated this sensor by applying soft-iron and hard-iron calibrations. Also, we applied moving average low pass filter to regulating accelerometer raw data. Time and rotation of device collected from device clock and IMU respectively. After calculating the contexts, to displaying appropriate information according to the user's context, we define different Levels of Detail. This system is implemented on the 3rd floor of the Geomatics faculty at K.N. Toosi University and developed on the Android platform with Java programming language. A performance test was carried out to evaluate the performance of the system. In each application run by different users, we collected Random Access Memory(RAM) and Central Processing Unit(CPU) usage for context-aware and non-context-aware systems. The results of the performance test showed that the average RAM and CPU usage in the context-aware system respectively 37.81% and 1.83% are less than non-context aware. Also, we used a questioner and asked ten users to evaluate the system’s UI, the performance of the context-aware system, and the non-context-aware system. The results showed that users have significant satisfaction in the performance of the context-aware system.},  
Keywords = {Context-aware Augmented Reality, Context Aware System, Indoor Positioning, Pedestrian Deadreckoning (PDR)},
volume = {10},
Number = {3}, 
pages = {111-133}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-863-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-863-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Shami, S. and Khoshlahjeh, M. and Ghorbani, Z. and Moghimi, A. and Mohammadzadeh, A. and SabetGhadam, S. S.},  
title = {Evaluation of Air Pollution Contributes for the COVID-19 pandemic in Iran using Sentinel 5 Satellite Data}, 
abstract ={One of the economic challenges facing developing countries is the cost of tackling air pollution and improving its quality. On the other hand, in addition to cost, the health of people in the community and existing diseases are also directly related to air quality. Therefore, the study and analysis of changes in air pollutants, including nitrogen dioxide, carbon monoxide, and ozone can provide valuable information for experts to analyze air quality. The presence of high-resolution spatial sensors to study a variety of applications has enabled experts in this field to study most environmental phenomena. In the present study, temporal and spatial changes of air pollutants were determined using Sentinel-5 satellite data in April 2016, simultaneously with the release of Covid-19 virus for Iran, and compared with the values of the same period in 1998. The spread of Covid-19 virus during this period had a variety of consequences that led to a reduction in factory activity as well as a decrease in vehicle traffic. Therefore, the present study seeks to investigate the effect of these factors by analyzing the temporal changes and spatial distribution of pollutants in the interval between these two times. The results confirm the improvement of air quality in this period compared to the previous year, April 1998. According to the results, the concentration of nitrogen dioxide in the entire barley column and tropospheric nitrogen dioxide , which is directly related to transportation and human activities, has decreased.},  
Keywords = {Covid-19, Air Quality, Air Pollutants, Sentinel-5},
volume = {10},
Number = {3}, 
pages = {135-146}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-962-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-962-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Ghasempoor, Z. and Behzadi, S.},  
title = {Traffic Modeling and Prediction Using Basic Neural Network and Wavelet Neural Network Along with Traffic Optimization Using Genetic Algorithm, Particle Swarm, and Colonial Competition}, 
abstract ={It is a fact that people are often looking for a way that combines the parameters of shortness, low cost, and low energy consumption. Hence traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people often prefer along with low traffic than a short one with heavy traffic. Therefore, it is clear that the main criterion for choosing a route is the traffic situation in the relevant route. Traffic has become a major social problem in all societies today. Understanding the causes of traffic and its aggravation parameters can reduce traffic problems. Meanwhile, the issue of traffic forecasting has become a goal among different nations. Since traffic can be predicted, it is possible to avoid wasting energy and time, which has become a crisis in metropolises today. But predicting traffic conditions and behavior, especially in large cities, requires management, planning, and using technologies such as GIS. In recent years, the urban transportation network has become more complex in modern societies. The reason for this is the creation of different infrastructures with the motivation of creating more convenience for the movement of citizens. The high complexity, multi-layered nature, and multi-structured nature of the urban, transportation network do not make it easy for citizens to move, and these factors may even confuse citizens more than just moving from one place to another. There is a direct link between transportation and traffic. So far, urban plans have been made to improve the traffic situation. The variability of the parameters affecting the traffic situation and its direct impact on the traffic problem has always been a big problem for different communities. Therefore, these parameters should be identified and the role of each of them on the traffic situation should be measured. Then it is possible to improve the traffic situation. To achieve this issue, the role of Geographic Information System (GIS) to solve problems that have a specific spatial and temporal dimension (such as traffic) should not be overlooked. This highlights the need for the present research to collect traffic data. If the goal of the research is achieved, it will save time, money, and energy at least. To measure traffic behavior in metropolitan areas and to achieve up-to-date traffic data, there is a need to provide and use methods to analyze traffic behavior. By achieving this goal, transportation can be prospered and the economic burden can be reduced on different communities every year. For this reason, solutions for traffic forecasting should be sought. In the meantime, the use of science called neural networks can be very practical. In this research, first, a system was designed to collect the traffic data required for the research. The issue of access to traffic data has always been a problem, which is concerned in this area. Therefore, in the present study, first, by designing a system for collecting traffic data, the desired problem was solved. In the next step, the collected traffic data is called and normalized. Then the error rate was calculated using test and training data. At this stage, the error rate was 72%. In the next step, traffic data analysis was performed using a combination of baseline and wavelet neural network, and the error rate was then calculated. The results show that using wavelet transforms is more accurate, but the error values ​​were calculated using test and training data, 28% due to the smaller number of inputs. In other words, the desirability rate was about 72%. Finally, the collected traffic data were optimized using optimization algorithms and the best point was calculated with the least possible error for each optimization algorithm.},  
Keywords = {Predicting Traffic Behavior, Neural Network, Wave Conversion, Optimization Algorithms},
volume = {10},
Number = {3}, 
pages = {147-163}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-958-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-958-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Mohsenifar, A. and Mohammadzadeh, A. and Moghimi, A.},  
title = {An Integrated Unsupervised Change Detection Method Based on the Discrete Wavelet Transform Fusion and An Improved Markov Random Field Model}, 
abstract ={Change detection is one of the most important processes in photogrammetry and remote sensing, in which occurred changes in a same geographical area are identified over time. Forests are one of the national assets of any country that has a vital role in climate change, groundwater formation and prevention of floods and soil erosion. Thus, an accurate change detection method should be exploited to monitor and maintain forest regions. In this paper, an efficient unsupervised change detection method is proposed for this purpose. Here, two bitemporal sattelite images, acquired at the forest areas of Unitted stasted and Australia are employed  to evaluate the proposed change detection method. In the first step of the proposed approach, discrete wavelet transform was used to generate an efficient change index by fusing of two difference images derived by NDVI and GNDVI vegetation indices. Anisotropic diffusion filtering was then applied to obtain robust change index in which noises was reduced while change regions was highlited. In the next step, the generated index was segmented into changed and unchanged classes using an improved k-means algorithm. Finally, improved MRF model initialed with the initial change map is employed to generate final change map. The proposed MRF model include two novel improvements in the main energy function, resulting in preserveing changed region details. The proposed improved MRF contained superiority of 0.49% and 0.61% compared to traditional MRF in datasets 1 and 2, respectively. The proposed MRF also outperformed Otsu, PCA-kmeans, GAFCM and GMMMRF methods, so that reduced the total error rate by an average of 0.93% and 5.31% in data sets 1 and 2, respectively. In general, the proposed method has a high capability for accurate identifying changes for vegetated areas.},  
Keywords = {Forest Change Detection, Discrete Wavelet Transformation, Diffusion Filter, K-means, Neighborhood Information},
volume = {10},
Number = {3}, 
pages = {165-182}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-937-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-937-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Karimipour, H. and Alesheikh, A. A.},  
title = {Location of Solar Power Plants by Combining the Best-worst Methods, Danp, Copras and TOPSIS Case Study of Fars Province}, 
abstract ={Consumption of non-renewable resources such as oil, gas and coal is increasing every day. But when these resources run out, we have to look for renewable energy such as solar, wind and geothermal. Solar energy is a clean energy that can reduce the production of environmental pollution and is the beginning of reducing carbon from human life. One of the most important issues in the construction of solar power plants is to determine the places that have a high potential for solar energy. This research has been done in order to locate solar power plants using multi-mechanical decision-making methods of participation in Fars province. In the initial studies on the region and the current state of solar power plants, and then using the resources and opinions of experts, 10 effective criteria, including climatic, geomorphological and economic criteria in the location of solar power plants have been selected. Criteria information layers are prepared in GIS environment and then the best-worst and Denp methods are used to weigh the selected criteria. In the first method, two criteria of sunshine hours and distance from roads and in the second method, the criteria of sunshine hours and distance from the fault have been performed as the most important and the least best criteria, respectively. Due to the different methods of these two techniques, the results of its losses have been used for the final weight of the criteria. After weighing the criteria, zoning has been done and 8 places have been proposed for the construction of a solar power plant in Fars province. In the last step, using Coopras and TOPSIS methods, the places whose proposals are ranked and for the final results, the integration of these two methods with the ranking technique is used. The methods used in this research are programmed in Linux and MATLAB software environments. Finally, the No. 8 reality site in Zarrin Dasht city of Fars province has been selected as the best location for the construction of a solar power plant.},  
Keywords = {Solar Power Plant Site Selection,  GIS, Multi-Criteria Decision Making Methods, Best - Worst, Danp, Copras and Topsis},
volume = {10},
Number = {3}, 
pages = {183-199}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-987-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-987-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {HeidariMozaffar, M. and Varshosaz, M. and SaadatSeresht, M.},  
title = {The Best Localization for Terrestrial Laser Scanner by Using Particle Swarm Optimization Algorithm}, 
abstract ={For complete 3D modeling of a desired area, using by terrestrial laser scanner point cloud, it is necessary moving the set and increase the occupation points to measure the occlusions. But it takes more time and money for field measurements and in result will be increased time and calculations cost. Thus, the initial planning for selecting the optimal locations for the device in order to complete 3D model is essential and the computing field and office costs in a reasonable period decreased. In this paper, particle swarm optimization algorithm to achieve this objective has been used. In the proposed method, an approximate model of the scan region needs for the candidate deployment positions, and makes the algorithm’s search space. Each particle is set of the selected candidate points and a set of particles is considered as a groups. Cost function was considered with two goals, a reduction in occlusions and pick a least possible number of selected points. Algorithms starts with a set of multiple random selection of points, as an initial response and moving particles in the search space, during successive iterations, the algorithm answers locating the optimal laser scanner. In this process, the optimal choice system is automatic and repetitive and ensure proper alignment with the minimum number of points required for a complete measurement of region is achieved. The results show that the particle swarm optimization algorithm in a large number of the candidate can optimize the laser scanner selected points for the establishment.},  
Keywords = {Optimal Locating, Particle Swarm Optimization, Terrestrial Laser Scanner, Occlusion},
volume = {10},
Number = {3}, 
pages = {201-219}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-314-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-314-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {Malek, M. R. and Malek, M. and Malek, M.},  
title = {Digital Contact Tracing: A solution  to Prevent the Spread of Corona Virus}, 
abstract ={COVID-19  has a profound effect on modern human life, and it is unclear what its future will be. Till now, 8417100 people were infected and 451661 people died. For many years, contact tracking has been the main way to prevent the spread of infectious diseases, especially the isolation of infected people. For example, The eradication of smallpox was achieved not by universal immunization, but by exhaustive contact tracing to find all infected persons. Contact tracing is the process of identification of persons who may have come into contact with an infected person and subsequent collection of further information about these contacts. The significant growth of positioning technologies, mobile computing, and wireless networks has led us to a new era of contact tracing, so-called digital contact tracing technology. The most important challenge facing people is the lack of privacy-preserving of these systems. on the one hand side in a centralized approach, the main problem with digital contact tracing regards the type of information which can be collected from each person and the way related data is treated by companies and institutions. On the other hand, public health responsible people need as much as possible accurate and complete data.},  
Keywords = {Contact Tracing, Covid-19, Geolocationing, Proximity Sensing, Location Based Services (LBS)},
volume = {10},
Number = {3}, 
pages = {221-228}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-955-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-955-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}

@article{ 
author = {sadeghian, S. and Rajabi, A. and Sedighpoor, D.},  
title = {3D modeling for documentation, restoration and determination of the area of Dolatabad Castle in Qom using UAV images}, 
abstract ={Cultural heritage is precious and irreplaceable capital that the importance of documentation in preservation and conservation of this heritages is clear to every person. The production of a digital model and 3D documentation of these heritages due to the shortcomings of traditional methods, is significantly expanding. The development of sensors, data collection methods, and the promotion of 3D visualization techniques, along with the presentation of diverse algorithms in computer vision, have had a tremendous role in 3D documentation But the vacancy of a comprehensive and integrated study that evaluates all three-dimensional documentary disciplines and presents a suitable process to achieve a controlled output is well felt. Over the past years, there have been papers on documentation of cultural heritages with a variety of tools and methods that, after reviewing them from 2000 to 2018, methods have been evaluated for collecting 3D data, visualization, software and modeling algorithms. active and passive methods in 3D modeling have been compared with 8 criteria; time, quality, time flexibility, cost, data density, geometric precision, performance in large locations and noise levels. It should be noted that selecting the appropriate method according to the objective and conditions of the region is possible. In the visualization section, standards and templates have been compared with eight criteria; geometry, based on XML, topology, building texture, showing complications,semantic information, descriptive information, web content, and georeferencing. In the software section, open source, commercial, and cloud computing software has also been reviewed. Due to the advantages and limitations of UAV photogrammetry mentioned earlier, flight planning for UAV photogrammetry projects and 3D documentation with it is more complex and influenced by several factors, so in UAV photogrammetry project, all aspects must be considered. the studied area is Dolatabad historical castle in Dolatabad of Qom Province in the center of Iran. This village castle is located in the west of Anar Bar (Qamroud) river, which, of course, has decreased with the construction of 15 Khordad dam. Apparently, in the past, this castle was one of the castles of the main villages of this region, around which crops such as wheat, barley, melon, and sunflower were cultivated, but today only wheat and barley are produced. Dolatabad castle with a longitude of "656´34 ° 49 and latitude" of 922´19 ° 32 and an altitude of 1547 meters above sea level. dimensions of area is more than 100000. this castle belongs to the Safavid era and has its own unique architecture but unfortunately it has been damaged over time. The most important pathological factors that have caused damage to the castle in past years are two important and influential factors of natural and human damage. after the 3D documentation of the castle and its restoration, using the 3D model obtained to determine the privacy of the fortress, and the protective, intuitive and functional boundaries are examined and the proposed privacy is provided and the boundary of the area is determined and the necessary factors for the Cadastre of Cultural Heritage are provided. Following the three-dimensional documentation of the Dolatabad historical castle and restoration and determination of the boundary, the necessary pre-requisites for registering the fortress as a national heritage were prepared and the Dolatabad historical castle with a registration number of 30,533 as a national heritages of Iran was recorded. A digital model is also made out of the castle, which can be used to create virtual museums or to rebuild and repair of castle again.},  
Keywords = {3D Documentary, Cultural Heritage Cadastre, Privacy, Restoration and Reconstruction, UAV Photogrammetry},
volume = {10},
Number = {3}, 
pages = {229-241}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-970-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-970-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2021}  
}