@article{ author = {JamshidMoghadam, H. and MohammadyOskuei, M.}, title = {An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data}, abstract ={The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum is composed of several components that make it a mixed spectrum due to the low spatial resolution observed of the employed sensors or the presence of multiple materials in its instantaneous field of view (IFOV). The existence of a mixed spectrum severely prevents the accurate processing of the hyperspectral data. Therefore, it is necessary to separate these mixtures through the so-called spectral unmixing methods. Spectral unmixing is performed to decompose a mixed pixel in hyperspectral images into a set of spectra (endmembers) and their abundances. Typically, two types of spectral mixing models (linear and nonlinear) are considered. In the linear mixing model (LMM), the reflected radiance at the sensor is the outcome of interference with one material, where a pixel is assumed to be a linear combination of endmembers weighted by their abundances. The nonlinear model, on the other hand, is used when the mixing scale is microscopic or materials are mixed intrinsically. In recent years, the linear mixing model has been a very popular model for hyperspectral processing in the last decades, and a large effort has been put into using this model for unmixing applications, resulting in an overabundance of linear unmixing methods and algorithms. Over the last decades, the linear mixing model has been utilized in the detection of minerals and their abundances. However, as early as 40 years ago, it has been observed that strong nonlinear spectral mixing effects are present in many situations, for instance, when there are multi scattering effects or intimate mineral interactions. While such nonlinear unmixing techniques have received much less attention than linear ones. Therefore, this paper aims to give an overview of the majority of nonlinear mixing models and methods used in hyperspectral image processing, and many recent developments in this field. Besides, several of the more popular nonlinear unmixing techniques are explained in detail. In this regard, nonlinear unmixing methods can be categorized into two groups: physics-based methods and data-driven techniques. The most important methods of these two groups are divided into bilinear and multi-linear models, intimate mineral mixture models, radiosity based approaches, ray tracing, neural network, kernel methods, manifold learning, and topology methods. A comprehensive review of these methods can be found in which bilinear and multi-linear models and neural networks have become more popular among researchers over the years. The current study should give the reader that is interested in working with nonlinear unmixing techniques a reasonably good introduction into the most commonly used methods and approaches.}, Keywords = {Remote Sensing, Hyperspectral, Nonlinear Unmixing, Bilinear Models, Intimate Mineral Mixture, Neural Network, Topology}, volume = {10}, Number = {1}, pages = {1-26}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-846-en.html}, eprint = {http://jgst.issgeac.ir/article-1-846-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Khoramak, S. and TabibMahmoudi, F.}, title = {Urban Vegetation Recognition Based on the Decision Level Fusion of Hyperspectral and Lidar Data}, abstract ={Introduction: Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the object classification and recognition results. Recently, hyperspectral and Lidar data has been used for vegetation covers classification. The spectral information derived from hyperspectral data is used to classify and identify the vegetation cover. However; due to the spectral similarities between various vegetation types, false positive results are increased. Using relief information extracted from Lidar data can solve these kinds of errors and can be very efficient for improving the object recognition results. Spectral similarities and spatial adjacencies between various kinds of objects, shadow and occluded areas behind high rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in vegetation recognition among other objects in urban areas. Therefore, new procedures and higher levels of modifications should be considered for improving the object recognition results. In recent years, the multi-agent systems have been considered as one of the most powerful tools for solving the problems of automatic object recognition in urban areas. Method: According to the difficulties of vegetation recognition in complex urban areas, the proposed object recognition in this paper is a decision level fusion strategy between hyperspectral and Lidar data based on the capabilities of the multi-agent systems. Vegetation indices from hyperspectral image are used as spectral features in the knowledge base. Moreover, digital surface model which is produced from Lidar data is used for height features extraction. After producing a rich knowledge base containing the spectral and height based features, the proposed hierarchical classification is performed which is composed of two steps; step 1: initial vegetation candidate recognition, step 2: vegetation classification based on the capabilities of the multi-agent systems. Applying the optimum thresholds on the normalized difference vegetation index in the first step produces a binary image containing the initial vegetation candidates. The multi-agent system in the second step of the proposed method in this paper contains several object recognition agents (one agent per each vegetation cover type), a coordinator agent and a yellow page. The object recognition agents have three layered internal architecture and use the belief-desire-intention (BDI) reasoning model.  Results: The capabilities of the proposed multi-agent vegetation recognition algorithm in this paper is evaluated based on the hyperspectral and Lidar data collected from the University of Houston and the surrounding areas. Four object recognition agents are defined for trees, healthy grass, water-stress grass and artificial grass. These four object recognition agents perform their reasoning based on the pre-defined spectral and height features in the knowledge base. The obtained results indicate the overall accuracy of about 87% from the proposed multi-agent hyperspectral and Lidar decision fusion strategy. The obtained results from performing the same multi-agent system only on the hyperspectral image (without considering Lidar data) have the overall accuracy for about 71%.}, Keywords = {Vegetation Recognition, Multi-agent System, Lidar Data, Hyperspectral Image, Spectral and Height Features}, volume = {10}, Number = {1}, pages = {27-37}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-838-en.html}, eprint = {http://jgst.issgeac.ir/article-1-838-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Allahyaribek, S. and safdarinezhad, A. R. and Karimi, R.}, title = {Hyperspectral Image Classification Based on the Fusion of the Features Generated by Sparse Representation Methods, Linear and Non-linear Transformations}, abstract ={The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there are a numerous challenges in reliable extraction of information from these images. The issues such as 1- spectral similarity of different phenomena, 2- sensor noises and atmospheric effects, 3- the effects of high dimensionality in the pattern recognition algorithms, 4- the necessity of large number of training data to perform a reliable classification, and 5- spectral variability of similar phenomena could be considered as some of the challenges in hyperspectral data processing. Decreasing of the high dimensionality effects via the dimension reduction algorithms (e.g. band selection and feature extraction algorithms), as well as increasing the separability of the overlapped classes through the linear/non-linear mappings into the feature spaces with the higher dimensional are two opposite and conventional approaches of hyperspectral data processing. These approaches would be used based on the factors such as 1- complexities of classes in the imaging area, 2- spectral range of imaging sensor, and 3- the restrictions of processing algorithms. In this paper the fusion of these two approaches is used to perform an accurate hyperspectral image classification. To do so, a novel feature extraction method is proposed to be used in the hyperspectral image classification. The core of this method is the fusion of the linear, non-linear and sparse representation based features which is used to produce the effective features in the weighted K-Nearest Neighbors (KNN) classification method. In this procedure, a set of supervised and nonlinear features are extracted as the first step through the Nonlinear Principal Component Analysis (NLPCA). The supervised usage of NLPCA in order to extract features is known as one of the novelties of this paper. In this step, the spectral bands are usually mapped to a high dimensional feature space through the self-estimator artificial neural networks (ANNs) which are trained separately by ground truth data. In the second step, the previously extracted features are linearly transformed by the Linear Discriminate Analysis (LDA) method in order to reduce the dimension of the hypercube generated via supervised NLPCA to a separable feature space. In the last step, a set of features which is proportional to the number of classes is generated based on the sparse representation theory. The sparse representation features were hired to handle the effects of the inter-class variability. The precisions of the classified features in the two different hyperspectral images were on average shown 6 percent improvements in comparison with the spectral bands and the other combinations of extracted features. Furthermore, reach to the approximately 99% overall accuracies in the classes with the few training data could be considered as other achievements of the proposed method.}, Keywords = {Hyperspectral Image, Classification, Sparse Representation, Feature Extraction}, volume = {10}, Number = {1}, pages = {39-53}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-894-en.html}, eprint = {http://jgst.issgeac.ir/article-1-894-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {MohammadiAsiyabi, R. and Saheni, M. R.}, title = {Palarimetric Synthetic Aperture Radar Image Classification using Bag of Visual Words Algorithm}, abstract ={Land cover is defined as the physical material of the surface of the earth, including different vegetation covers, bare soil, water surface, various urban areas, etc. Land cover and its changes are very important and influential on the Earth and life of living organisms, especially human beings. Land cover change monitoring is important for protecting the ecosystem, forests, farmland, open spaces, water quality, wildlife habitat, and human health. Uncontrolled changes in land cover (deforestation, extensive agricultural activities, vast urban and exurban development, etc.) will not only destroy the natural environment and the ecosystem but also will affect different aspects of human life including the economy. As a result, various effective methods for land cover monitoring are always deliberated by researchers. Remote sensing based techniques, due to their unique capabilities and extensive data availability, have attracted great interest for decades. In addition, fully Polarimetric Synthetic Aperture Radar (PolSAR) images, due to the rich data and the ability of day-night and all weather condition data acquisition, has got high capability to study and monitor land cover changes. Recent developments of SAR sensors in acquiring fully polarimetric, continuous, and very high resolution data, besides enabling precise land cover monitoring, has necessitates the usage of more powerful and robust algorithms in different image processing steps. Classification algorithms are among the most fundamental tools of remote sensing image processing; and an increasing number of researches have been focused on developing novel and more robust classification methodologies for remote sensing data in recent years. Mid-level representations have also been introduced to the remote sensing data classification to enhance the reasonability and semantic rationality of the land cover classifications through remote sensing techniques. Bag of Visual Words (BOVW) model, originally inspired by the Bag of Words (BOW) model from text mining, is one of the most authoritative mid-level representation models which has achieved state-of-the-art results in different image processing tasks. BOVW representation model has recently been introduced to remote sensing data processing and proved to be very beneficial for high resolution remote sensing data representation. In the present paper, a novel segment-based BOVW framework has been developed for PolSAR data representation and classification. The obtained classified maps are quantitatively and qualitatively compared with the result of classification with well-known classifiers including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Wishart Classification Algorithms. The main contributions of the current study are (i) covering the semantic gap between low-level features extracted from the PolSAR image and high-level concept of land cover in remote sensing data classification (ii) introducing novel segment-based BOVW framework for PolSAR image classification, and (iii) extensive evaluation of the performance of the BOVW model for PolSAR data classification in terms of the model parameters and low-level features selection. The experimental dataset is acquired by RADARSAR-2 from San Francisco Bay, California, in C band and at fine quad-pol mode, in 2008. The achieved overall accuracy, 90.1%, illustrates the capability of the proposed model for SAR image classification purposes. Moreover, the proposed segment-based framework for the BOVW model has decreased the undesired speckle effect of the SAR data, without speckle filtering.}, Keywords = {Synthetic Aperture Radar, Classification, Bag f Visual Words, SVM}, volume = {10}, Number = {1}, pages = {55-64}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-780-en.html}, eprint = {http://jgst.issgeac.ir/article-1-780-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Sadooghi, E. and Taleai, M. and Abolhasani, S.}, title = {Simulation and Evaluation of Urban Development Scenarios Using Integration of Cellular Automata Model and Game Theory}, abstract ={Urban growth is a dynamic and evolutionary spatial and social process that relates to the changes of urban spatial units and the transformation of people’s lifestyles and consequently demographic changes. Considering the urban development process as a function of land uses interactions, population structure and the strategic behavior of the agents involved in the urban development process (the Developer as the investor and the Municipality as the planner and regulator), asking for developing a framework to capable modeling and analyzing these driving actors. This study is presented a model based on the integration of cellular automata, multi-criteria analysis and agent-based Game theory for land use planning and simulation of urban development using two urban population changing scenarios. This integration is used as a tool for modeling and quantifying land uses interactions with respect to their service region. Moreover, in the proposed model, land-use type change is defined as the outcome of the interaction between two decision-makers (agent): Developer and Municipality. Game theory is used to model and quantify the strategic behavior of these two agents. The developer’s strategy is to develop spatial units that have more suitability due to their neighborhood externalities and current demand for each land use type while the municipality’s strategy is to protect the zoning regulations based on the master plan and land use suitability. The utility functions are defined based on each agents’ preferences, the game structure for each spatial unit is composed based on developer priorities (as proposer) and the bargaining situation between agents involved in the development process (as a sequential game) is solved using backward induction process. Utilizing Game theory will result in finding an optimal and rational strategy for both agents in developing land-use types for each permissible spatial unit. The methodology presented in this study proposes a rational and realistic approach for urban land use planning and simulating urban growth. To evaluate the performance of the proposed model, it was implemented in the region of Roozieh in the city of Semnan. Two scenarios were modeled based on the Semnan’s approved master plan. The suitability of developed land-use types is evaluated using multi-criteria analysis. In order to provide a more efficient evaluation method, the outcome of the implementation of each scenario using the proposed method was evaluated to provide a basis for comparing results based on the concept of spatial equity. The results demonstrate the ability of the model to simulate the outcome of urban land-use development plans based on the regulations and guidelines of the master plan, especially for the balanced distribution of services and service availability.}, Keywords = {Urban Development Simulation, Multi-Criteria Analysis, Cellular Automata, Agent-Based Model, Game Theory}, volume = {10}, Number = {1}, pages = {65-82}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-877-en.html}, eprint = {http://jgst.issgeac.ir/article-1-877-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Zamani, R. and MashhadiHossainali, M.}, title = {Comparison of Local and Non-Local Methods in Covariance Matrix Estimation by Using Multi-baseline SAR Interferometry and Height Extraction for Principal Components with Maximum Likelihood Approach}, abstract ={By today, the technology of synthetic aperture radar (SAR) interferometry (InSAR) has been largely exploited in digital elevation model (DEM) generation and deformation mapping. Conventional InSAR technique exploits two SAR images acquired from slightly different angles, in which the information of elevation and deformation can be captured through processing of the phase difference of the images called interferometric phase. Depart from undeniable efficiency of interferometric SAR processing technique (InSAR), some main issues such as phase unwrapping ambiguity may limit its applications and its accuracy in height mapping. However, in the frame of multi-baseline interferometry and by the availability of more than one interferogram of the same region these problems can be overcome. Multi-baseline SAR interferometry are hence of great interest and can be successfully exploited for automatic phase unwrapping and high quality DEM reconstruction. This paper focuses on stacks of interferometric SAR data as they are used as input to multi-baseline framework for the purpose of height estimation and compare the results of such local and non- local covariance matrix estimation methods achieved by same data and on the same area, where the information of estimated covariance matrix is employed in the elevation mapping. In local methods such as Boxcar, a fixed-size window is considered for the central pixel which do not consider the statistical homogeneity of neighboring pixels, so this method in non-homogenous area leads the results to lower accuracy. In non-local methods the procedure is centered around the idea of checking the pixels to find the same statistical distribution as the investigated pixel, which is realized by Wishart similarity function. In this case, all the similar pixels are then used to estimate the complex covariance matrix of the reference pixel. In the context of non-local filtering, one of the most efficient method is NLSAR approach, which has been considered in our framework. More precisely, NLSAR uses samples in a search window and assigns each pixel a weight based on its similarity to the target pixel. The idea of NLSAR approach is to find, within a search window, for each pixel p to be filtered non-local neighbors t that share statistical similarity with the considered pixel. A pixel t is assumed to come from the same statistical population as the considered pixel p, if the patches or local neighbors that surround the two pixels are similar. The similarity for two pixels is defined as a likelihood-ratio test based on the hypothesis that their two Wishart distributed covariance matrices are equal. The main peculiarity that made the NLSAR approach extremely popular is its ability of filtering noise while preserving structures and discontinuities. The stadium height estimated by using the covariance matrix estimated with Boxcar and NLSAR methods respectivey is equal to 41.100 and 42.5400 meter which in the comparison with the actual height extracted from AfriSAR mission, indicates higher accuracy for the results of NLSAR method. The task of covariance matrix estimation is so challenging for complex area such as the area used for this paper which contains the Angondjé stadium in Mondah, Gabon, that represented a complex scenario because of the occurrence of layover, a phenomenon that gives rise to the interference within the same pixel of ground scattering mechanisms located of different height with  same slant range distance. we use PCA (Principal Component Analysis) to decompose principal scattering mechanisms. Then , the powerful statistics Maximum Likelihood (ML) technique is used to properly compute the elevation information of the principal components by the available information of covariance matrix. From this covariance matrix, both amplitude and interferometric phase values extracted which are then used for height estimation.}, Keywords = {Synthetic Aperture Radar (SAR), Height Estimation, SAR Interferometry, Multi-baseline, PCA, Covariance Matrix Estimation}, volume = {10}, Number = {1}, pages = {83-95}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-865-en.html}, eprint = {http://jgst.issgeac.ir/article-1-865-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Toosi, A. and Dadrasjavan, F. and Samadzadegan, F.}, title = {Object Level Strategy for Spectral Quality Assessment of High Resolution Pan-sharpen Images}, abstract ={Panchromatic and multi-spectral images produced by the remote sensing satellites are fused together to provide a multi-spectral image with a high spatial resolution at the same time. The spectral quality of the fused images is very important because the quality of a large number of remote sensing products depends on it. Due to the importance of the spectral quality of the fused images, its evaluation is also important. This paper presents an object-based strategy for evaluating the spectral quality of fused images, aiming to overcome the limitations of the current pixel-based method. This type of assessment is conducted by focusing on homogeneous objects with similar spectral and textural behaviors. In the implementation phase of the article, after determining an optimal spectral metric, the proposed object-based strategy is applied to five datasets from four different satellite sensors types, and the spectral behavior of the fusion methods has been studied in several image classes. The results indicate that the spectral behavior of the fusion methods does not follow a deterministic rule. Finally, statistical analyses were used to determine the best fusion algorithms in each class, and a list of superior algorithms in different classes was provided to researchers in the field of remote sensing and image fusion. This approach will help the scientific community to take a realistic vision at choosing the fusion algorithm appropriate to their satellite imagery.}, Keywords = {High-resolution, Image Fusion, Spectral Quality Assessment, Object-based}, volume = {10}, Number = {1}, pages = {97-110}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-849-en.html}, eprint = {http://jgst.issgeac.ir/article-1-849-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Azizi, R. and Sattari, M. and Momeni, M.}, title = {A New Dictionary Construction Method in Sparse Representation Techniques for Target Detection in Hyperspectral Imagery}, abstract ={Hyperspectral data in Remote Sensing which have been gathered with efficient spectral resolution (about 10 nanometer) contain a plethora of spectral bands (roughly 200 bands). Since precious information about the spectral features of target materials can be extracted from these data, they have been used exclusively in hyperspectral target detection. One of the problem associated with the detecting process using hyperspectral data is the spectral variation due to topography variability and spectral mixing. Moreover, imperfect sensor noises and atmospheric influences on the target radiance together lead the observed spectral feature of the same material to change in different situations. Target detection methods model the spectral variation in order to compensate their effects on the process. Statistics and subspace based approaches are the two most important methods used in detection process. Statistics and subspace based approaches are the two most important methods used in detection process. Using special statistical assumptions and modeling the spectral variation with limited number of parameters are the main disadvantages of these methods. One of the strongest detection method is the sparse representation method. It models the differences in the spectral features of targets and background using dictionary matrices. Indeed, it constructs a complete subspace of materials spectrum and their variations. Building a pure dictionary (clean of spectral mixing) is the main challenge associated in the sparse representation method in the detecting process. Three methods- the dual windows, the global and the learned dictionary- have been introduced in literature. In the dual windows, since it uses outer window to select the target pixels, spectral mixing has not been cleaned. In the learned dictionary as it uses random picked pixels in order to learn the dictionary, the risk of spectral mixing exists. Furthermore, spectral mixing exists in general method. Considering the disadvantages of the aforementioned methods, in this thesis we introduce a new method to construct the dictionary. Not only do the dictionary atoms provided by this method construct a complete subspace and model spectral variation, but they also are as pure as possible.  In the proposed method, it is tried to achieve two main purposes which are forming the background subspaces and minimizing the spectral mixing of atoms in the dictionary and target. To this end, correlations between target spectrum and all image pixels are calculated. Afterwards, using image pixels which have different degrees of correlation with target spectrum, different dictionaries are created for the background. Finally, a dictionary is selected from the created dictionaries which presents a complete subspace of image and the subspace also has the lowest correlation with the target spectrum. In this paper, the proposed method of making a dictionary along with a sparsity model, called SRBBH is used and introduced as method Proposed+SRBBH. To survey the efficiency of the proposed methods, a simulation data set and three real data were used, and in order to evaluate the methods, the area below the ROC chart level was used. In experiments performed on both Cuprite and Sandiego data, the area under the graph was 0.9997 and 0.9961, respectively, which shows higher values than other methods. For the other two sets of data, the proposed method performs better than other methods of target detection.}, Keywords = {Target Detection, Hyprspectral Data, Sparse Representation Methods, Spectral Variation , Dictionary}, volume = {10}, Number = {1}, pages = {111-132}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-825-en.html}, eprint = {http://jgst.issgeac.ir/article-1-825-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {KaffashCharandabi, N. and Gholami, A.}, title = {Developing Ubiquitous Road Accident Hazard Map (Case Study: Tabriz- Marand Road)}, abstract ={Modeling a road accident hazard zoning map to identify high-risk areas is a very effective step to reduce the resulting casualties. Due to the dynamic nature of many of the factors affecting the identification of these areas, traditional zoning mapping does not seem to be effective. In the field of ubiquitous modeling in the framework of the GIS, it is possible to produce a separate map at any time, any place, for any user and under any circumstances that is more compatible with his or her changing individual and environmental conditions. In this study, in a hybrid model of data mining methods based on 22 environmental and individual contexts the probability of accident risk was calculated for each user. Thus, by obtaining the accident data recorded in Tabriz Marand road in 2019, the required pre-processing was done based on T2 statistic and PCA method. Then, based on GRNN and the collected data, the optimal network was trained and then evaluated with test data. In addition, eight different scenarios were designed for this case study and at 3008 points of this road, the risk of accident was predicted for each scenario. The results showed more than 90 % accuracy for the proposed model of this study. According to the results of different scenarios, the 5 km area around customs and 3 km around Sufian city have the highest risk in this road, which can be verified by 50 accidents happened in this area during the mentioned period.}, Keywords = {Ubiquitous GIS, Accident, Zoning Map, GRNN, Context Awareness}, volume = {10}, Number = {1}, pages = {133-143}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-920-en.html}, eprint = {http://jgst.issgeac.ir/article-1-920-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Fathi, M. and MokhtarZade, M. and Safdarinezhad, A. R.}, title = {An Automatic Detection of the Fire Smoke Through Multispectral Images}, abstract ={One of the consequences of a fire is smoke. Occasionally, monitoring and detection of this smoke can be a solution to prevent occurrence or spreading a fire. On the other hand, due to the destructive effects of the smoke spreading on human health, measures can be taken to improve the level of health services by zoning and monitoring its expansion process. In this paper, an automated method is proposed to detect the dilute smoke caused by large fires in multispectral images. The main idea of this method is the impossibility of precisely reconstructing the smoke in the bands affected by smoke (blue band) using regression models from other spectral bands. In the first step of the proposed method, the absolute value of the residuals of the regression estimation of blue spectral band is transformed into a binary mask with the help of Otsu thresholding. Afterwards, in an iterative process, non-smoke areas are detected and then clustered. In the iteration process, a regression model is fitted for each cluster and for each pixel, coefficients with the least error of the blue band reconstruction is used. Through more accurate estimation of the blue band, it reduces the effect of First Positive Error and leads the mask of residuals obtained from thresholding process to the smoke areas. The final step of the proposed method is to refine and remove the incorrect image segments. This method has been successful in detecting diluted smokes and also in disregarding smoke in non-smoky images. The results show the average accuracy of  99.04 percent in several datasets with diluted smokes.  }, Keywords = {Smoke Detection, Linear Regression Model, Iterative Clustering, Smoke Detection, Otsu Tresholding}, volume = {10}, Number = {1}, pages = {145-157}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-892-en.html}, eprint = {http://jgst.issgeac.ir/article-1-892-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ranjbari, M. R. and Vagheei, R. and Bigdeli, B.}, title = {Mapping the Potential of Groundwater Resources in Hard Formations Using Geographic Information System and Remote Sensing, Case Study: Northwest of Shahroud}, abstract ={In recent years, rapid population growth has led to increase per capita water use in various sectors including agriculture and industry and a growing gap between water demand and water supply has emerged. Therefore, identifying and tracking changes in groundwater resources as an alternative and reliable source of surface water resources are so important to region located in the Middle East with dry weather and large volumes of drought and climate changes. In the current study, the potential and evaluation of water resources are investigated in the west and northwest formations of Shahroud in an area of about 480 square kilometres using remote sensing and spatial information techniques. In this region, due to the presence of carbonate rocks as well as the function of erosion and tectonic forces in parts of the region, relatively suitable aquifers have been formed. For this purpose, information layers including lineament, lithology, slope, aspect, waterways, precipitation and type of precipitation were evaluated by Analytical Hierarchy Process (AHP) method. In this study, information layers were prepared using the ability of RS and GIS in three main stages: extraction of geological map, extraction of area lineaments and extraction of other information layers. The proposed methodology applied two different types of remote sensing sensors including Landsat 8 as optical data and Sentinel-1 as radar data. In preparing lithology map with emphasis on calcareous formations, the proposed method applied four techniques including independent component analysis (ICA), minimum noise fraction (MNF), band ratio (BR) and color composition on Landsat 8. Using the pixel purity index, the endmemberes were extracted and the obtained maps were classified by Support Vector Machine (SVM) and maximum likelihood (ML). Among the classification methods, SVM has a higher ability to classify than ML and identified formations with higher accuracy in the region. Finally, tree decision making system was used to improve the classification of images. The geology of the area was extracted in four classes focusing on calcareous formations. The map was compared with the geological map prepared by the surveying organization and the following results were obtained: Kappa coefficient 0.83, Accuracy of Ku and Jd Formation (calcareous formations) with 99.2% accuracy, Shemshak Formation (Js) with 80.2% accuracy, Lalun Formation (Cl) with 81.2% accuracy and Alloviume (Q) with 78% accuracy were identified. Subsequently, lineaments area were extracted using integration of Landsat 8 and Sentinel 1 radar remote sensing data based on semiautomatic methods. Based on the results, the lineaments were in good agreement with the faults in terms of orientation and numbers at different lengths. Also, the densities of the lineaments extracted in different formations of the geological map of the region had 99% compliance with the densities of faults in the formations. Therefore, by combining band6 of Landsat8, VV and VH polarization Sentinel1, the area lineaments can be extracted with high accuracy. Other thematic layers were extracted by using remote sensing and GIS Techniques using 30 m SRTM. It was also observed that the groundwater potential map is mainly controlled by precipitation, lithology, and lineament density factors.}, Keywords = {Groundwater Potential Mapping, GIS, Remote Sensing, AHP, SVM, ML}, volume = {10}, Number = {1}, pages = {159-181}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-883-en.html}, eprint = {http://jgst.issgeac.ir/article-1-883-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Mohammadi, M. and Hosseinali, F.}, title = {Analyzing and Comprising the Location of SIX Universities in Tehran Using Geospatial Information Systems}, abstract ={Universities as the places of training the experts play an essential role in social, economic and cultural structure of the nations. The quality of education and the educational space are two major effective factors on the quality of training the students. Since the educational places are of the most important land-uses in the cities, the compatibility of them with other surrounded land-uses is vitally important in urban planning. In many cases the adjacency of educational places with incompatible land-uses such as military and industrial or locating in polluted areas or avoiding from necessary utilities have caused falling the learning abilities of the their students. In this research the desirability of the location of six universities of Tehran namely: K.N.Toosi university of technology, Iran university of science and Technology, Amirkabir university of technology, university of Tehran school of engineering, Shahid Rajaee University and Allameh Tabataba'i university has been evaluated and compared using the spatial analyzes in Geospatial Information System. To find the major criteria (factors), many students in those universities were asked. Based on the responses, the criteria were classified into two categories of physical and environmental. Satellite images, maps and census data were used to produce criteria maps. Evaluating and comprising between the criteria were done based on the idea of more than 500 students. Each of considered factors is extracted from a corresponding factor map and this is the point that highlights the role of GIS. For environmental factor three maps were produced and then combined. Greenness map in this study is a map showing the NDVI index of Tehran. To produce this map, image of Landsat 7 satellite, ETM+ sensor, band 3 and 4 was used as the input. ENVI software was used for this process and NDVI obtained within a thousand meters radius around each university. For the other map processes in this study ArcGIS 10.3 software was used. Next, three factor maps were combined using Index overlay method with the equal weights and environmental index map was created. Air pollution is at the highest level in winter. Noise pollution is significantly higher in the mornings. Real greenness is better observable in summer when all the trees have leaves. Different time of gathering will not affect the results because the relative conditions of the target universities are fairly constant during the process of collecting data. To prepare physical index map, accessibility and compatibility of surrounded land-uses must be determined.  Accessibility was extracted from a 1:2000 map of Tehran using OD-cost matrix in ArcGIS software. To generate compatibility, land-uses around the target universities were determined and their compatibility with university land-use was compared pair-wisely. The compatibilities expressed in five levels. Levels of compatibility have then been assigned different weight based on previous studies. Once the values of factors obtained, they were be combined to get the final index. However, the weights of each criterion must be assigned. AHP as a multicriteria decision making method and ratio estimation method were used for this task. At last, the final index for desirability of the location of target universities achieved by combining sub-criteria. The results of this research revealed that the location of Amirkabir University achieved the more desirability of location. After that K.N.Toosi, university of science and technology, university of Tehran school of engineering, Shahid Rajaee university and finally Allameh Tabataba'I stand on the next ranks. }, Keywords = {Location, Compatibility, Accessibility, Environment, University, Tehran}, volume = {10}, Number = {1}, pages = {183-197}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-895-en.html}, eprint = {http://jgst.issgeac.ir/article-1-895-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Asgari, O. and NeysaniSamany, N. and Atarchi, S.}, title = {Validation of Volunteered Geographic Information Landuse Change Using Satellite Imagery}, abstract ={Land use change monitoring is one of the main concerns of managers and urban planners due to human activities and unbalanced physical development in urban areas. In this paper, a combination of remote sensing data and volunteered geographic information was used to assess the quality of volunteered geographic information on land use and land cover changes monitoring. For this purpose, the ORBVIEW-3 satellite imagery for 2005, the IRS satellite for 2008, 2010, and 2015 and the Google Earth imagery from 2011 to 2019 have been used as the reference database. One of the issues discussed in the field of validating volunteered geographic information in monitoring land-use changes is related to the quality of this information. Various indices have been proposed for evaluating the quality of VGI parameters in the field of validation of land use changes monitoring, includes data completeness, spatial accuracy, and shape accuracy. This paper aimed to evaluate the quality of volunteered geographic information designed for land use changes monitoring and compare the results with satellite images as reference data. In other words, this study seeks to compare the results of volunteered geographic information and satellite imagery to show the accuracy of VGI . Actually, the use of local knowledge of individuals in the urban planning process and the importance of timely information for better management and decision-making in the face of urban progresses such as urban sprawl has led to attention and understanding the importance of VGI in land use change. To validate the VGI, first satellite images were obtained and clustered using unsupervised learning. The use of satellite imagery due to the repeatability with wide and multi-source view of an area increases the credibility and confidence of VGI quality validation. The VGI collection has been carried out over a web-based GIS which provided a facility for users to register personal information, parcel land use and draw polygons per relevant land use. Finally, volunteered geographic information quality was evaluated. In the first step of validation process, data completeness was assessed. According to the results, the value of VGI completeness has been changed during assessment period, as  by moving away from the present, the accuracy has been also reduced, so that in 2018, 72.7 percent of the volunteered data were completely overlapping with the reference data, but in 2007 this compatibility decreased to 25 percent. In the next step, the spatial accuracy of the data was calculated. The central distance method was used for evaluating this parameter. The results of the distances between the polygons centers of the volunteer data and the polygons centers of the reference data show that the maximum and minimum distance are 14.37 m and 0.09 m, respectively. In the last stage, the shape accuracy of the volunteered data was examined. Due to the standard deviation of 0.61, it can be concluded that the two databases differ significantly.}, Keywords = {Volunteered Geographic Information, VGI Quality Components, Satellite Imagery, Land-use Changes}, volume = {10}, Number = {1}, pages = {199-212}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-928-en.html}, eprint = {http://jgst.issgeac.ir/article-1-928-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shahmardani, I. and Aliabbaspour, R. and Chehreghan, A. R.}, title = {Evaluation of Updating Methods in Building Blocks Dataset}, abstract ={With the increasing use of spatial data in daily life, the production of this data from diverse information sources with different precision and scales has grown widely. Generating new data requires a great deal of time and money. Therefore, one solution is to reduce costs is to update the old data at different scales using new data (produced on a similar scale). One approach to updating data is to use the updated large-scale dataset as reference data to update small-scale datasets; in this way, the modified features are identified in the two datasets and then updated by modifying the changes to fit the small-scale dataset. In terms of the type of the updated feature, map updating issues in the vector dataset are divided into three categories: pointwise, linear, and polygon. One of the most important features of the class of polygonal features in urban environments are the buildings that are vital in urban maps and their updating process in urban applications has a high priority. In this research, an attempt has been made to study the issue of updating polygonal features from different perspectives by a careful and comprehensive examination. These perspectives include spatial clustering methods, pattern extraction, and updating methods.  Spatial clustering methods are classified into five methods: natural principles, partition-based, graph-based, Region Merging, and density-based approaches. Each clustering method have been used in different studies according to Gestalt criteria (proximity, similarity, and continuity). In pattern extraction, different types of patterns have been studied in various studies while the linear pattern extraction as a sample has been comprehensively examined in this study.   In the updating methods, three updating approaches including Propagating, Local, and Constraint-based are examined. In the Propagation updating approach, only large-scale data are updated, then these updates can be propagated into small-scale data. This update is especially used for the MRDB spatial database by propagating updates at various scales. Local updating consists of three steps: 1) Change detection between the recently updated large-scale dataset and the old small-scale dataset. 2) Integrating the discovered changes into the small-scale dataset (by quantifying and formulating these changes). 3) Ensuring that the consistency is maintained. In Constraint-based updating approach which demonstrates the necessities of this research, first, by grouping the buildings, useful information such as the area of ​​the buildings, the average and the standard deviation of the separation distance between the buildings is obtained. Then, with the appropriate operators, Constraint-based generalization is performed to update the maps.  There are various criteria for evaluating the updating methods, while the Precision and Recall criteria have been used to evaluate the effectiveness of these problems. Precision criterion examines the ratio of the number of correctly matched features to the total number of features that that have been matched correctly or incorrectly. Recall criterion evaluates the ratio of correctly matched features to the total features that have been matched correctly or not matched. In other words, in the Precision criterion, the number of features that are incorrectly matched and in the Recall criterion, the number of features that are not matched, are effective in comparing the two criteria. The categorization mentioned in each perspective and the advantages and disadvantages of different methods are also presented in this research.}, Keywords = {Vector Dataset, Updating, Building Polygon Features}, volume = {10}, Number = {1}, pages = {213-226}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-983-en.html}, eprint = {http://jgst.issgeac.ir/article-1-983-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Rezaei, M. and Vafaei, A. R. and Rasti, R.}, title = {Providing a New Approach to Optimize Combined Scale Factor Allocation Insurveying Projects}, abstract ={Nowadays, given the widespread use of global projection systems such as the UTM projection system in surveying projects relying on elliptic referents, finding a suitable composite scale factor for each project to implement construction projects using mapping equipment such as devices Total station is necessary. Unfortunately, today the method of calculating and assigning average composite scale factor in survying projects is not fully scientifically performed, and usually inappropriate composite scale factors are calculated and applied, causing unacceptable errors in the project. In this research, an executive project was investigated and a number of scaling factors were calculated for it. The results showed that the method that calculated the scale factor by considering the whole project scope in three dimensions yielded the most accurate case. Evaluation of the accuracy of work showed that the highest error was related to the average scale factor in a thousand meters length, 0.048 m to the east and west end of the project and 0.002 m to the center of the project. It was also found that if the permissible error of the mean scale factor is 5 mm, about 1266 m can be survey from the project center east and west with the same mean scale factor and if more than That range of surveying activity should be done, either more error must be accepted or another average scale factor calculated for the new range.}, Keywords = {Surveying Projects, Combine Scale Factor, UTM Projection System, Optimization}, volume = {10}, Number = {1}, pages = {227-236}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-984-en.html}, eprint = {http://jgst.issgeac.ir/article-1-984-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Miraki, M. and Sohrabi, H. and Fatehi, P. and Kneubuehler, M.}, title = {Comparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images}, abstract ={Abstract: Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aerial vehicles (UAV) have been attended to be an easy-to-use, cost-effective tool for the classification of trees. In fact, given the cost-efficient nature of UAV derived SfM, coupled with its ease of application, it became a popular choice. The type of imagery is an important factor in classification analysis because the spatial and spectral resolution can influence the accuracy of classification. On the other hand, classification algorithms also play an important role in the accuracy of tree species identification. So, this study investigated the performance of four classifiers for tree species classification using UAV-based high-resolution imagery in broadleaf forests and takes a comparative approach to examine the three non-parametric classifiers including support vector machines (SVM), random forest (RF), artificial neural network (ANN), and one parametric classifier including linear discriminant analysis (LDA) classifiers in heterogeneous forests of Noor city located in Mazandaran province. In June 2019, the study area was photographed. The field survey was carried out to record the species and position of the mature overstory trees which were clearly identifiable on the orthomosaics. Individual tree crowns were clipped by one-meter buffer and the digit numbers were summarized at for each tree by computing descriptive statistics from the orthomosaics. Using zonal statistics, mean, standard deviation, variance, unique, range, mode, and median were calculated for raw bands (Red, Green, Blue), vegetation indices (NRB, NGB), and band ratios (G/R, R/B) from RGB orthomosaics. We classified the tree into 4 classes: Parrotia persica (Ironwood tree), Populus capsica (Caspian poplar), Ulmus minor (Common Elm), and Quercus castaneifolia (Chestnut-leaved oak). Finally, the classification algorithms were applied using R software. The classification accuracy for identified trees was performed using 10-fold cross-validation by computing the producer’s accuracy, user’s accuracy, and Overall accuracy. All algorithms resulted in overall accuracies above 80%. Of course, the results showed that, as a parametric algorithm, LDA with an overall accuracy of 0.87 provided the best results for tree classification, because it does not require the tuning of free parameters. As for parameter value, the mean was the most important that this can be related to the similarity of this feature in any sample. Caspian poplar with user accuracy of 0.97 and Ironwood tree with user accuracy of 0.72 had the highest and lowest classification accuracy, respectively. Caspian poplar high accuracy is probably due to its crown color which is quite different from the other species. The main error (misclassification) is a classification between “Ironwood tree” and “Common Elm” classes. This may be caused by the fact that the spectral signatures between Ironwood tree and Common Elm trees are very similar. In general, our study showed that UAV derived orthomosaic can be used for tree classification with very high accuracy in mix broadleaf forests by different algorithms.}, Keywords = {Linear Discriminant Analysis, Support Vector Machine, Random Forest, Artificial Neural Network, UAV, Spectral Indices}, volume = {10}, Number = {2}, pages = {1-10}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-926-en.html}, eprint = {http://jgst.issgeac.ir/article-1-926-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Hassanpour, P. and Feizizadeh, B. and ValizadehKamran, Kh.}, title = {Readiness Assessment of Implementation of Enterprise Spatial Data Infrastructure in Agriculture Jahad Organization of West Azarbaijan Province}, abstract ={Spatial data infrastructure (SDI) refers to a basic collection of technologies, policies, and organizational arrangements which creates a platform for sharing location information for users at all levels of the organization up to national and international. patial Data Infrastructure (SDI) is known as a fundamental comprehensive approach of spatial data managing and sharing. Due to the complexity of establishing SDI, creating SDI has been considered as serious challenge, especially in developing countries. Successful and sustainable SDI can be developed when social, organizational and cultural issues are resolved in harmony with the technological ones. The main goal of SDI implementation is to overcome the problem of duplication of data collection by organizations, which leads to wasting financial funds and time. SDIs have been developed for use at global, regional, national, state and local levels.  Technically, SDIs involve a very intricate digital environment, such as a wide range of geospatial databases, networks, standards, metadata, institutional structures and technologies.  The framework of the present research is constructed on the basis of a survey and an SDI readiness model. Following a review of the research background, and taking into account the interaction of relevant indicators, criteria for establishing SDI were identified. This research aims to apply readiness assessment of SDI implementation in enterprise SDI approach. To achieve this goal, the agricultural-Jahad organization was elected as case of enterprise SDI. In this context, within the first step the affecting factors for the successful SDI-implementation were identified and requested data were collected in the form of a questionnaire during the specialized interviews with experts, mangers and decision makers. In this regard, the statues of organization related to spatial data availability, motivation, skill, perception, policy, financial resources, organizational structure, technology, human resources and information status were assessed. Accordingly, the analytical network process (ANP) was applied to compute the criteria weights and determine the significance of each factor for successful SDI implementation. The criteria weights were obtained and decision model was accordingly organized. The overall results also indicate that there is a low level of awareness of SDI in the organization and also one of the major challenges for the organization is the lack of specific policies and guidelines for data generation, data storage and sharing. However, one of the key strengths for successful SDI implementation in Jahad organization is the high motivation and telecommunications platforms for data exchange between organizations and appropriate computer systems. Subsequently, the possibility of successful implementation of SDI was evaluated and analyzed using the Likert scale and the results showed that the minimum chance of SDI implementation in pessimistic mode was 60.7% and in optimistic mode 85.37%. Summary of the results of need assessment and feasibility studies show that it is possible due to SDI deployment conditions in Agriculture Jahad organization of West Azarbaijan province. Due to the large number of stakeholders, data sharing, technology and network requirements, developing SDI is a very cost-effective technology. In every society, but especially in developing countries like Iran, there are also a significant number of threats affecting the development of SDI. SDI-readiness assessment leads to the identification of challenges and issues, and therefore helps to minimize their impacts on successfully developing SDI. Thus, we conclude that the results are of great importance for analyzing the factors which may have significant impact on successful SDI development in Agriculture Jahad organization of West Azerbaijan province.}, Keywords = {Spatial Information Infrastructure (SDI), Readiness assessment, Agricultural Jihad, West Azerbaijan}, volume = {10}, Number = {2}, pages = {11-21}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-914-en.html}, eprint = {http://jgst.issgeac.ir/article-1-914-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Izakian, Z. and Mesgari, M. S.}, title = {A Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach}, abstract ={In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences, and data mining techniques provide useful solutions to solve this problem. Nowadays, clustering technique as the most widely used function of data mining, has attracted the attention of many researchers in various sciences. Due to different applications, the problem of clustering time series data has become highly popular and many approaches have been presented in this field. An efficient clustering method groups data in such a way that the objects in the same cluster are more similar to each other than to objects in different clusters. In order to compute the difference/similarity between time series data in clustering process, a similarity measure or distance function is used. Therefore, choosing an appropriate distance function is one of the most important challenges that should be considered before starting the clustering process. So far, various distance functions have been proposed to measure the difference/similarity between time series and each of them have its own strengths and weaknesses. Since choosing a suitable distance function to cluster a specific data set is a complicated process, in this study, we proposed a clustering method based on combination of the well-known Fuzzy C-Means (FCM) method and the Particle Swarm Optimization with the ability of using different distance functions in time series clustering process. In this way, the step of choosing the best distance function before starting time series clustering procedure has been deleted and different similarity measures can participate in the clustering process with different impacts. The objective function in this study is defined based on Fuzzy C-Means clustering objective function and the particle Swarm Optimization algorithm is used to find the optimal value for the considered objective function. Finally, by considering three distance functions including Euclidean distance, dynamic time warping and Pearson correlation coefficients the proposed method was implemented on seven well-known UCR time series datasets. Also, by considering the average normalized mutual information as a criterion for evaluating the performance of methods in this research, the proposed method was compared with five other methods. The results of this comparison indicated that the method presented in this study performed better in more than 85% of cases rather than other methods. In order to have a better evaluation, Tukey’s multiple comparison tests with a threshold of p < 0.05 is used with the ability of comparing the methods in pairs. The results obtained by Tukey test showed that, in about 83% of cases, the difference between achieved results by the proposed method in this study and results obtained by the other five techniques are statistically significant. Overall, the results of this study clearly showed the superiority of the proposed clustering method in the production of high quality clusters in comparison to some other methods.}, Keywords = {Clustering, Time Series, Particle Swarm Optimization, Fuzzy C-Means}, volume = {10}, Number = {2}, pages = {23-37}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-941-en.html}, eprint = {http://jgst.issgeac.ir/article-1-941-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Kaveh, M. and Yazdi, M. and Dehghani, M. and Sharzei, M.}, title = {Mitigation of Tropospheric Delay on InSAR Interseismic Displacements}, abstract ={One of the major challenges of Interferometric Synthetic Aperture Radar (InSAR) technique is the existence of tropospheric effect on the results. The tropospheric effect is due to the changes of atmospheric parameters including temperature, pressure, and humidity between the master and slave images. In this research, two different methods based on spatial-temporal filters and calculation of phase delay using MERIS data were evaluated. The main objective is to monitor the interseismic deformation across the Tasouj fault located in West and East Azarbaijan provinces. To this end, Stanford Method for Persistent Scatterer (StaMPS) is applied on 12 ascending ENVISAT ASAR images spanning between 2004 and 2008. The deformation time series obtained from StaMPS are two rough due to the atmospheric effect. In the first method employed for the atmospheric effect reduction,  considering that the atmospheric effect is a temporally-decorrelated and spatially-correlated signal, two different high pass and low pass filters are applied in the temporal and spatial space in order to extract the atmospheric signal which is then subtracted from the time series analysis results. In the second method, the phase delay due to the troposphere is estimated using the MERIS water vapor product. GPS measurements are finally used in order to evaluate the performance of different atmospheric reduction methods. Two quantitative criteria, i.e. Root Mean Square Error (RMSE) calculated from GPS and corrected time series and standard deviation of atmospherically-corrected time series are considered as two relevant methods for evaluating the fluctuations reduction. The method based on spatial-temporal filters shows more superiority over the other one with RMSE and standard deviation of 13 mm and 40 mm, respectively, at its best case.  }, Keywords = {Persistent Scatterer, Tropospheric Corrections, GPS, MERIS, Interseismic}, volume = {10}, Number = {2}, pages = {39-56}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-897-en.html}, eprint = {http://jgst.issgeac.ir/article-1-897-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Sadeghi, B. and Samadzadegan, F. and Dadrasjavan, F.}, title = {3D Classification of Urban Features Based on Integration of Structural and Spectral Information from UAV Imagery}, abstract ={Three-dimensional classification of urban features is one of the important tools for urban management and the basis of many analyzes in photogrammetry and remote sensing. Therefore, it is applied in many applications such as planning, urban management and disaster management. In this study, dense point clouds extracted from dense image matching is applied for classification in urban areas. Applied images are acquired using a Micasense RedEdge multispectral camera to increase the classification accuracy. The band to band registration is one of the existing challenges of multi-spectral camera, which the SIFT algorithm is used to extract the corresponding features of each band. One band selected as reference and other bands are transferred to the reference band by projective transformation. Finally, the bands are combined to create a color image from each three bands. So, two point clouds are generated using dense image matching techniques from two sets of images. To produce a multi-spectral point cloud, the two set of point clouds have been integrated using nearest neighbor interpolation. The multi-spectral point clouds are classified by using random forest algorithm, structural and multi-spectral features. This process composed of three parts as structural information, multi-spectral information, and integration of both. Finally, the results are shown a 25% improvement in the accuracy of the integration of multi-spectral and structural information compared to multi-spectral information and 32% improvement in the accuracy of the integration of multi-spectral and structural information compared to structural information. Classification using visible information (RGB) instead of multispectral information resulted in an accuracy drop by 5%.}, Keywords = {3D Classification, Dense Image Matching, Multispectral Image, Band to Band Registration, Point Cloud, Random Forest}, volume = {10}, Number = {2}, pages = {57-78}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-898-en.html}, eprint = {http://jgst.issgeac.ir/article-1-898-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ahangarha, M. and SaadatSeresht, M. and Shahhoseini, R. and Seyyedi, S. T.}, title = {Crop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images}, abstract ={Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional neural networks and hyperspectral imagery, here is to identify the planting area because these networks have an excellent performance in achieving change detection. In this research, we investigate to use of deep learning methods in comparison with another tradition methods for obtaining changes in an agricultural area so that, after generating difference images with the use of Otsu algorithm we generate a preliminary binary map. Then we extracted the feature by using sparse auto encoder networks and classified pixels in two categories to change and no change by using the convolutional neural networks too. In the end, we obtain a final change map by making a model and evaluation of accuracy. That we have achieved even better results, which indicates the need to use deep learning methods. Since solving, the problem manually related to change detection. To investigate capable of the proposed method, 2 datasets hyperspectral imagery from the American Hermiston agricultural fields in the United States was used and vegetation cover near the Shadegan wetland located in the south of Khuzestan province, evaluated by the Hyperion sensor. The proposed method compared to other methods has an overall accuracy of 95% and the kappa coefficient of 0.86.}, Keywords = {Change Detection, Deep Learning, Hyperspectral Images, Sparse Auto Encoder, Agriculture Monitoring}, volume = {10}, Number = {2}, pages = {79-89}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-860-en.html}, eprint = {http://jgst.issgeac.ir/article-1-860-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Shariyari, H. and Emami, H.}, title = {Forest Fire Potential Modeling and Simulation of its Extension Using Remote Sensing Data and GIS: (A Protected Area of Arasbaran)}, abstract ={Forest fire models are generally used in different aspects of fire management and are helpful in understanding and prediction of fire behavior. Forest fires cause a significant damage for public property by destroying a large tract of forest.  This helps fire fighters to focus on an area with greater risk and to develop better substructure for fire fighter training and ultimately to plan fire-fighting policies to minimize damage and stay safe. In the same way simulation modeling also provides an adequate tool to estimate risk when actual risk data are limited or unavailable. Ultimately there is a need to model forest fire in ground, crown, and surface fuel. Forest fire risk assessment, which based on an integrated index, becomes an important tool for forest fires management. The integrated index includes the information about fuel, topography and weather condition which constitute potential fire environment together. The fuel and weather condition are essential for forest fire occurrence, so the main potential fire environment parameters in the process of the forest fire risk assessment are temperature, fuel moisture content and vegetation status. The environment parameters data for traditional forest fire risk assessment were always obtained from the weather station. In present study forest fire risk was estimated as the proportion of simulation runs that burned a particular point and was accumulated over the entire study area. Study used satellite remote sensing datasets in conjunction with topographic, vegetation and climate datasets to infer the causative factors of fires. Spatial data on all these parameters have been aggregated and organized in a GIS (Geographic Information System) framework.  In this research, the relation between the most effective environmental elements (vegetation index (VI), Land surface temperature (LST), slope, aspect, wind speed and direction) and human factors (vicinity of roads and residential areas) has been investigated as a mathematical model with the occurrence and release of fire in the forest protected area of Arasbaran. In order to validate the results, the data from previous fire burns has been used.To this end, LDCM satellite imagery, digital elevation model, wind speed and direction, and other parameters were used in synthesis remote sensing and geographic information systems. At first, a combination of environmental factors, fire hazard maps and map of areas with a 50% fire risk was produced. Then to simulate its extension, Alexandria's semi-experimental models and cellular automation algorithms were used and the genetic algorithm is used to optimize the model parameters. The obtained results of normalized correlation coefficients of environmental parameters showed that VI, LST, slope and aspect were 29.20%, 29.11%, 21.93% and 19.75%, have the greatest correlation with the risk of fire map, respectively. In addition, about 17% of the study area have a high fire risk potential and more than 50% of the area is in a high fire hazard. In addition to environmental elements, the study of the relation between human factors and fire risk showed that the proximity to the road had the highest share in the incidence of fire. Also, the simulation results of synthesis of the Alexandros semi-experimental and cellular automation models showed that expansion of fire in the first region of the test have an overall accuracy 95.56% and kappa 91.41% and an overall accuracy 62.69% and Kappa of 13.13% compared to the reference data in the second region of the test. These results were in good agreement with the results of the simulation studies in firefighting development. Therefore, the simulation process can be used to protect the forest effectively. Results from the current study were quite significant in identifying potential active-spots of fire risk, where forest fire protection measures can be taken in advance.}, Keywords = {Forest Fire, Simulation of Fire Spread, Arasbaran Region, Remote Sensing}, volume = {10}, Number = {2}, pages = {91-109}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-817-en.html}, eprint = {http://jgst.issgeac.ir/article-1-817-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Ghadimi, M. and Derakhshan, A.}, title = {Behaviour Comparison of the Taleqan Dam Using Instrumentation Data and Radar Interferometry}, abstract ={Dams are one of the most important fundamental structures all around the world. Given the high volume of water in the dams, they are susceptible to damage and destruction, leading to large financial losses and fatality. Therefore, by installation of instrumentation data in the dam’s body and execution of terrestrial surveying network, stability and safety of dams can be monitored. However, occasionally abnormal processes take place in the dam’s body and foundation that do not match with the instrumentation data’s outcomes. In the recent years, with the evolution and progress of radar imaging techniques and the image processing approaches, large deformations in the dams’ and bridges’ body can be monitored precisely. In the current study, the abnormal deformation and displacement taken place within the time period of Sentinel-1A 2014-2018 on the downstream part of Taleqan dam’s body were evaluated 4 mm/y and were compared with instrumentation data. The results imply that the occurred displacement is related to the external and protective layer of the dam’s body (Riprap) and has no correlation with the dam’s body behaviour.}, Keywords = {InSAR, Behaviour of Taleqan Dam, Sentinel1-A,}, volume = {10}, Number = {2}, pages = {111-117}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-873-en.html}, eprint = {http://jgst.issgeac.ir/article-1-873-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Abbasi, O. R. and Alesheikh, A. A.}, title = {Georeferencing Semi-Structured Place-Based Web Resources Using Machine Learning}, abstract ={In recent years, the shared content on the web has had significant growth. A great part of these information are publicly available in the form of semi-strunctured data. Moreover, a significant amount of these information are related to place. Such types of information refer to a location on the earth, however, they do not contain any explicit coordinates. In this research, we tried to georeference the semi-structured resources on the web using machine learning. To this end, we leveraged the advertisements related to real state domain in the city of Tehran, Iran, published in Divar website. In order to extract the advertisesments from the website, a crawling approach was chosen. In addition, to assign coordinates to advertisements, we used Random Forests algorithm. The results show that using this approach, the advertisements can be georeferenced at the precision of neighborhoods. The resulting presicion from this approach is about 2 km and 6 km in latitude and longitude directions, respectively. Moreover, the results demonstrate that price of the property has higher importance relative to other variables considered in this study. It can be concluded that the price of properties in Tehran shows stronger spatial pattern in North-South direction than East-West direction.  }, Keywords = {Georeferencing, Place-Based Data, Random Forests, Web Resources}, volume = {10}, Number = {2}, pages = {119-129}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-927-en.html}, eprint = {http://jgst.issgeac.ir/article-1-927-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Heydarivaraste, S. and Emadi, S. R. and Jamour, Y.}, title = {Estimation and Analysis of Precipitable Water Vapor Using GPS Data and Satellite Altimeter}, abstract ={Determination of water vapor in the atmosphere plays an important role in forecasting weather conditions and precipitation studies. For this reason, it is very important to study the tropospheric delay, especially the wet component, which is due to the presence of water vapor in the atmosphere. In this paper, the amount of water vapor was estimated by altimeter satellite radiometer and GPS data, which were based on GPS results and compared with satellite altimeter results. For this purpose, observations of 16 and 133 transmissions of Jason-3 satellites with a period of 10 days in 2018 were used. After processing the altimeter satellite observations using BRAT 3.3 software, the average amount of precipitation water vapor in this method for Tonekabon, Urmia and Bandar Abbas cities was 45, 44 and 30 mm, respectively. In the GPS method, using the Precise point positioning algorithm (PPP), the total tropospheric delay in the vertical direction was obtained (ZTD) and then the hydrostatic delay (ZHD) was subtracted from the total delay and finally by applying the relevant conversion factor to Non-hydrostatic delay (ZWD), the amount of precipitating water vapor was estimated.With processing GPS observations of three permanent stations of Tonekabon, Urmia and Bandar Abbas in 2018 corresponding to the observations of 16 and 133 transmissions of Jason-3 satellite with a time interval of 10 days and using From Bernese 5.2 software, the average amount of precipitable water vapor was estimated to be 47, 45 and 31 mm, respectively. Finally, the amount of RMS and standard deviation from the two methods were estimated to be 1 to 1.5 mm and 5 to 5.5 mm, respectively. The closeness of the results obtained from the two methods shows a very high agreement and compatibility between these two methods with a correlation coefficient of about 0.98 and the ability to combine them for climate and weather studies.}, Keywords = {Precipitable Water Vapor, Troposphere , GPS , Satellite Altimetry, Jason Satellite}, volume = {10}, Number = {2}, pages = {131-139}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-942-en.html}, eprint = {http://jgst.issgeac.ir/article-1-942-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Hashemi, V. and Mesgari, M. S. and MohammadiKazaj, P.}, title = {Solving the Ride-Sharing Problem with Non-Homogeneous Vehicles by Using an Improved Genetic Algorithm with Innovative Mutation Operators and Local Search Methods}, abstract ={An increase in the number of vehicles in cities leads to several problems, including air pollution, noise pollution, and congestion. To overcome these problems, we need to use new urban management methods, such as using intelligent transportation systems like ride-sharing systems. The purpose of this study is to create and implement an improved genetic algorithms model for ride-sharing with non-homogeneous vehicles (like taxis and vans with a capacity of 4 and 10 passengers). The proposed genetic algorithm can group passengers according to their trip similarity based on Spatio-temporal parameters to reduce the number of empty seats in vehicles, followed by the number of vehicles through the city, and get the optimal traveling path for each group of passengers. Optimal traveling path planning should also be considered to minimize each group's travel distance and, as a result, the time delays during the trip for each passenger and driver. Therefore, in this algorithm, four objective functions are considered. The objectives include minimizing the total travel distance of trips, the entire time delay (deviation from ideal times) at the origin and destination of passengers, number of vehicles, and number of empty seats. Due to the previous studies and the lack of combination of these objective functions mentioned above, in this article, complete research was conducted to create a model by combining these objective functions. Combining these objective functions complicates the model and, consequently, presents challenges in its implementation. To overcome these problems, Two innovative mutation operators and two local search algorithms under the titles of genetic algorithm and innovative algorithm based on passengers' travel time priority proposed to improve the genetic algorithm's exploitation ability and to reach the global optimum answer. The first innovative mutation operator is called join-vehicles. This innovative operator aimed to reduce the number of used vehicles by using vehicles' maximum capacity to serve passengers. As discussed in this paper, conventional mutation operators such as insert or scramble operators do not have adequate ability to solve this problem. In this innovative method, the indices of two genes related to two random vehicles' positions in the entered chromosome are chosen randomly. The goal is to remove the second vehicle and combine its passengers with the first vehicle to travel beside its passengers. Also, the arrangement of boarding and disembarking this set of passengers is planned in a way that the car's capacity condition is always satisfied; therefore, there will no longer be a restriction on passengers' combination in a van with a taxi vice versa. The second innovative mutation operator was proposed to change the van into a taxi. During the training, we would observe that some vans were used to serve the passengers while less than half of their capacity was occupied. At first, this operator replaces the van vehicles on the input chromosome with random taxis not used on the chromosome and recalculates this chromosome's cost with the new state. If this new state reduces the chromosome's cost, the taxi will be replaced with that van in the chromosome. Another issue that arises after applying the mentioned mutation operators on the chromosome is how to take turns for passengers to board and disembark in altered parts of the chromosome to respond to requests optimally. Therefore, two local search algorithms based on the innovative passengers' time priority to board and disembark and the traditional genetic algorithm have been implemented to increase the solution quality. These two algorithms are applied to the altered part(s) of the input chromosome and replace the resulting output with this/these part(s). About the innovative local search algorithm based on the time priority of boarding and disembarking passengers, a passenger whose expected time to board is earlier than the other passengers of a group gets on the vehicle first. This passenger's expected time to get off at his destination is compared with the other passengers' expected time to board if he/she has higher priority than the others. Then the vehicle reaches him/her to his/her destination first. Then the vehicle goes to the origin of the next passenger, who has more priority to get in the vehicle. According to passengers' expected time priority, this procedure is repeated to board and disembark them properly. In this model, 18 travel requests, including 26 passengers (some travel requests included more than one passenger), were considered, which want to be transferred in a hypothetical road network that contains 46 nodes and 75 edges. Finally, the implemented model was tested and evaluated during six different scenarios. The results indicate the efficiency of the implemented model of this paper. It should be noted that according to the chromosome encoding method used in this paper, which is similar to some previous studies, the model of this paper can be used, tested, and evaluated in other areas related to the vehicle routing problem.}, Keywords = {Ride-Sharing, Improved Genetic Algorithm, Metaheuristic Mutation Operator, Local Search Algorithm}, volume = {10}, Number = {2}, pages = {141-163}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-923-en.html}, eprint = {http://jgst.issgeac.ir/article-1-923-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Nadri, M. and AkhoondzadehHanzaei, M.}, title = {Anomaly Detection in Time Series of Chlorophyll Around the Time and Location of Large Coastal Earthquakes Using Random Forest Method}, abstract ={Earthquake is one of the most devastating natural hazards which efforts to predict the time, location and magnitude of it have not been yet completely successful. Remote Sensing data is proved to be an effective source of information about lithospheric and atmospheric activities around the impending earthquakes which are referred to as earthquake precursors. The issue of detecting anomalies in these precursors has been interesting to many researchers. One of the precursors that has been taken into consideration by the researchers, is the chlorophyll-a (chl-a) concentration on the sea surface. Since, over %70 of the Earth's surface is covered by water and many seismic active faults are located in coastal belts of the continents, the behavior of oceanic earthquake-related parameters such as Sea Surface Temperature (SST), surface latent heat flux, upwelling index and chl-a, is of particular importance. Elastic strain in rocks, formation of micro-cracks, gas release and other chemical or physical activities in the Earth's crust before and during earthquakes has been reported to cause changes in oceanic parameters. Chl-a parameter is obtained through various methods including laboratory methods of spectroscopy, chlorophyll fluorescence measurement or through satellite data using Color Index (CI) and Raily band ratio (OCX) algorithms etc. Changes from time to time in plankton population in ocean surface and chl-which is the indicator of the primary productivity of phytoplankton biomass in the ocean, can be continuously monitored from space by Ocean Color sensors. In this study, MODIS on Aqua and Terra products were used to examine the pattern of variations of chl-a. By examining the chlorophyll time series of five large earthquakes produced by MODIS sensor products on Aqua and Terra platforms and using a random forest algorithm, it was observed that the release of thermal energy and ground gases due to the activity of Tectonic plates or other physical and chemical activities of the earth's crust before, during and after coastal and near-coastal earthquakes can lead to changes in the amount of chlorophyll in the water surface and this parameter can be used and investigated as an earthquake precursor in future research. The results showed that chlorophyll-a levels exceeded the permissible limits 51, 48, 46 and 28 days before the Gujarat earthquake by 85, 45, 15 and 35%, respectively. In the 2004 Sumatra earthquake in the 20 days before and 18 days after the earthquake, the percentage of chlorophyll-a parameter crossing the upper limit was 110 and 190, respectively. In the 2006 Java earthquake, 42 days before, 15 and 16 days after the earthquake, the amount of chlorophyll-a suddenly changed to 136.84, 52.63 and 107.89% of the allowable threshold. In two other studies, this amount is equal to 199.87, 25, 150 and 190% more than allowable limit, respectively, on the 44th and 34th days before, on the day of the earthquake and 13 days after the Chile earthquake, and 321.42, 50 and 160.71% more than allowable limit 7 and 4 days before and 17 days after the earthquake in Mexico.  In addition, the clear superiority of the Random Forest (RF) algorithm in correct detection of anomalies showed that RF algorithm can be introduced as an effective tool in anomaly detection in time series.}, Keywords = {Earthquakes, Chlorophyll-a, Random Forests, MODIS, Anomaly}, volume = {10}, Number = {2}, pages = {165-174}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-848-en.html}, eprint = {http://jgst.issgeac.ir/article-1-848-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {NekouzadeChaharmahali, E. and Asgari, J.}, title = {Accuracy Improvement of Tropospheric Delay Interpolation in RTK Networks}, abstract ={The effect of troposphere on the signals emitted from global navigation satellite system (GNSS) satellites, appears as an extra delay in the measurement of the signal traveling from the satellite to receiver. This delay depends on the temperature, pressure, humidity as well as the transmitter and receiver antennas location. In GNSS positioning, tropospheric delay effects on accuracy of different components of obtained coordinates. In RTK networks the amount of this parameter is determined by solving double difference observation equations between reference stations and then is interpolated for rover receiver. Tropospheric delay consists of a wet part and a dry part. The dry part that forms about 90 percent of total delay, is related to station height. So in the cases that the height of rover station is significantly different from the average height of reference stations, reduction in accuracy of interpolation is expectable. To investigating this issue, in this article we compared interpolation accuracy of double difference tropospheric delay in two networks with different structure. In both of networks, we have a central receiver that is surrounded with four other receivers. We considered the central receiver as rover station and the others as reference stations. The main difference between these networks is about stations height. In the first network that is named Sima, the difference between the height of rover station and average height of reference stations is 122 meters. The amount of this parameter is 1095 meters for the second network that is named Ebry. To comparing the accuracy of tropospheric delay interpolation in these networks, we determined zenith tropospheric delays (ZTD) for all stations by processing GNSS observations using CSRS-PPP (Canadian Spatial Reference System – Precise Point Positioning) online service. Then we selected the nearest reference station to rover as master reference station. In the following we identified the satellites that were visible in 100 epochs for all stations. Between these satellites, one of them with the most elevation angle was selected as reference satellite. ZTD’s were converted to slant tropospheric delay in satellite-receiver direction using global mapping function. Then double differenced tropospheric delays between the reference satellite and the others and between the master reference station and other reference stations, were determined. Finally this parameter was computed for the position of rover station using interpolation with a two parameter linear equation. After computing RMSE (Root Mean Square Error) of interpolated values, we found that the accuracy of interpolation decreased significantly in the second network. Therefore we can conclude that the difference between the height of rover station and the height of reference stations, has a direct effect on accuracy of tropospheric delay interpolation in RTK networks. So in the following of the article, we introduced a new method to eliminate height variations effects on interpolation accuracy of tropospheric delay. After using this method RMSE of interpolation decreases from 32 mm to 9 mm in the first network and in the second network decreases from 228 mm to 14 mm. in other words we have 69.2 and 93.7 percent of accuracy improvement in these networks. Due to these results, we expect a positive effect on positioning accuracy by applying this method in RTK networks.}, Keywords = {Interpolation, Tropospheric Delay, Double Difference Positioning, Network Real Time Kinematic, Precise Point Positioning}, volume = {10}, Number = {2}, pages = {175-188}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-950-en.html}, eprint = {http://jgst.issgeac.ir/article-1-950-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {Heydari, A. and Amerian, Y. and Mahbuby, H.}, title = {Detection and Modeling of Medium-Scale Travelling Ionospheric Disturbances in Iran Region}, abstract ={Ionosphere layer variations are divided into regular and irregular. Regular changes can be considered as daily changes, changes depending on latitude and changes due to solar activity. Travelling Ionospheric Disturbances (TID) is one of the irregular changes of ionosphere which categorized in small, medium and large scales. Medium-scale Travelling Ionospheric Disturbance (MSTID) which are propagated because of Atmospheric Gravity Waves (AGW) is the main obstacle for accurate interpolation of ionospheric correction in a Global Positioning System (GPS) network, so detection and simulation of these perturbations is necessary. The purpose of this paper is discovering MSTID using carrier phase, which in addition to the values of the total electron content recovered from the observations of GPS also confirm the values detected using the carrier phase observations. MSTIDs are waveforms that have parameters such as amplitude, velocity, direction and wavelength that extracting these parameters are goal of simulation of MSTID. Generally, MSTIDs are planar and longitudinal waves, so to calculate their parameters, first a profile of or dSTEC by constant latitude is considered, then by examining displacement of maximum values of these parameters in a period of time, velocity will be determined. To calculate wavelength, wavelet analysis was used. Results of  and TEC observations were almost identical. MSTIDs have movement in southwest-northeast direction by velocity of 100 meters per second and wavelength of 232 kilometers and amplitude of 0.02 TECU. It means that these perturbations cause an error of 4 millimeters in L1 measurement. Since, phase observation’s precision is 1 millimeter, this error value is significant.  However, the carrier phase observations can be measured with an accuracy of one hundredth of a cycle, which by multiplying this value by the wavelengths of the GPS signal will be about 2 mm. Therefore, error that occurs due to MSTID, it is significant and should be considered.}, Keywords = {MSTID, GPS, TEC, Carrier Phase}, volume = {10}, Number = {2}, pages = {189-198}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-946-en.html}, eprint = {http://jgst.issgeac.ir/article-1-946-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @article{ author = {HeidariMozaffar, M. and Varshosaz, M. and Saadatseresht, M.}, title = {Occlusion Area as Suitable Guidance for Terrestrial Laser Scanner Localization}, abstract ={Terrestrial Laser Scanner (TLS) technology, have altered quickly data acquisition for map production in surveying. In many cases, it is impossible to complete surveying of the desired area without TLS displacement in one station to another. Occlusion is innate in data acquisition, with this type of device. To solve this problem, TLS devices should be placed in different locations and scanning operation to be performed. Increase the number of scan stations cause data redundancy and on the other hand will be increases the computational and monetary cost of project. Aim of this paper is presents a novel method for selecting a better place and localization of TLS. Thus, the mechanism of data acquisition was considered by TLS. Also parameters affecting the choice of a place and a station were investigated. Point cloud data investigations show that these parameters have a major impact in reducing the time needed to completeness of data collection. Occlusion as one of the most important parameters has been discussed in this paper. Using data from one station with combination of image processing method, the area of hidden parts to be estimated. Due to the size and area of the various occlusion set, determines to be appropriate locations for new data collection station. After evaluating candidate points according to given criteria, conditional on the project, the next point will be selected for acquisition. By continuous using this method, reduce a ground operation volume in this type of projects. Reducing time of land surveying in many field projects is very important. While ensuring the quality of data and decreased project costs is essential.}, Keywords = {Terrestrial Laser Scanner, Occlusion, Localization, Canny Algorithm}, volume = {10}, Number = {2}, pages = {199-207}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-51-en.html}, eprint = {http://jgst.issgeac.ir/article-1-51-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2020} } @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} } @article{ author = {Bahadorizadeh, H. and Malek, M. R.}, title = {Proposed an Approach to Fit a Specialized Conceptual Model for Use in VGI System (Case Study: Surface Water Resources)}, abstract ={Today, volunteered geographic information (VGI) has provided an innovative, fast, and low-cost approach to data collection. Using this approach will lead to problems such as semantic heterogeneity. Ontology is one of the newest modeling scientific methods and that can solve the data semantic heterogeneity problems. But using ontology to collect information has challenges in volunteered geographic information systems. Since ontology is derived from the technical procedure so, for use it, specialized tools and specialized information are needed and its output will be used by experts. As people are not usually experts and do not have specialized tools, so in this research, we have proposed the method for utilizing ontology in VGI systems to prevent the creation of semantic heterogeneity in the data. Besides, volunteers don’t need to train or to have specialized tools for using this system. In this method, we are gathering information and developing the ontology model of information and finally an understandable conceptual model for the public at three levels of details using the classes, subclasses, relationships, and features in the ontology model will be built. Then we are relating this conceptual model to the ontological model using a look-up table. By doing this, the volunteers are entering their information in the VGI system based on the conceptual model, and the result of the ontology model will be provided to the decision makers as output. Finally, we selected the surface water resources as the case study and then evaluated the conceptual model from viewpoint of the simplicity and ambiguity of the volunteers and the lack of need for specialized information to use it. According to the results, approximately 83% of the concept model is unambiguous and understandable to volunteer without training. Also the results of the ontological model were evaluated in an efficiency viewpoint for decision making by experts. The results of this evaluation also are showing 88% of the output information from the ontology model is appropriate for decision making. Therefore, the results of the evaluating are showing the effectiveness of the conceptual and ontological model for the intended purpose.}, Keywords = {Volunteered Geographic Information (VGI), Semantic Heterogeneity, Ontological Model, Conceptual Model, Look-up Table}, volume = {10}, Number = {4}, pages = {1-16}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-977-en.html}, eprint = {http://jgst.issgeac.ir/article-1-977-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {EbrahimianGhajari, Y.}, title = {Design and Implementation of a Geospatial Model To Evaluate the Resistance of Urban Buildings to Earthquakes in Scenarios and Different Risk Conditions (Case Study: Region 6 of Tehran)}, abstract ={Considering that earthquakes are one of the most common natural crises in the world, especially in Iran, so far, a lot of research has been done to evaluate the vulnerability of buildings to earthquakes. The main problem in the face of an earthquake is the complete unpreparedness to deal with it, that one of the basic strategies to create this preparedness is to evaluate and be aware of their resistance to earthquakes in different risk scenarios and conditions. In this study, using the opinions of geologists, structures and earthquakes engineers, urban planning experts, passive defense and architecture engineers, 8 vulnerability criteria extracted and fuzzy standard criterion maps produced according to each of them. The Fuzzy AHP technique was also used to weigh the criteria and the fuzzy Simple Additive Weighting operator was used to combine the standard maps. To defuzzify fuzzy vulnerability maps and generate vulnerability maps in different risk conditions, the Ordered Weighted Averaging operator (OWA) was used. Analysis of the results showed that in the most optimistic scenario, 39% of the buildings have low vulnerability and in the most pessimistic scenario, 49% of the buildings have high vulnerability. Sensitivity analysis technique was used to evaluate the model, which showed that the results are highly reliable.}, Keywords = {Seismic Evaluation, Geospatial Information Systems (GIS), Region 6 of Tehran, Fuzzy AHP, OWA, Sensitivity Analysis}, volume = {10}, Number = {4}, pages = {17-33}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-975-en.html}, eprint = {http://jgst.issgeac.ir/article-1-975-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Etemadfard, H. and Sadeghi, V. and Aldirawi, N. and Shad, R.}, title = {Optimal Locating of Urban Parks Using GIS and Genetic Algorithm (Case Study: Samawah City, Iraq)}, abstract ={One of the important issues in urban management is locating of urban parks. Urban parks, as one of the most important public service spaces in the city, have a great role in promoting the social, cultural, economic and environmental conditions of urban areas. Optimization can be considered as an effective tool for this problem. The key factors to achieve a successful public park are accessibility and appropriate link between different features of urban structures. Therefore, it is necessary to identify effective criteria and appropriate tools in urban park site locating. The purpose of this paper is optimal locating of urban parks sites in the city of Samawah (Iraq) using Geo-spatial information system and genetic algorithms. To this end, objective functions include; minimize population movement distances and homogeneous distribution of people in proportion to the capacity of parks. Based on the proposed method and considered criteria; Land use, distance from urban transportation network, distance from rivers, population density and distance from noise (factories, etc.), four suitable areas for the construction of a park in the city of Samawah were determined. Scrutinizing of the characteristics of the determined sites for the construction of the urban parks, the high capability of the genetic algorithm in this application was proved. The northern part of the river has high population density. The genetic algorithm has chosen a location between the eastern and western blocks from the all candidates to cover the population demand. In the southern part of the river; three locations have been proposed for the construction of urban parks. The first and most important location is located in the central and high densely populated part of the city, where the existing barren land has provided ideal conditions for the construction of urban park. This location is the best option for building a park due to its proximity to various land use, proximity to building blocks and the densely populated area, as well as ideal access to the transportation network.  Third location; it includes the barren land covers to the west of the citychr('39')s residential area. Due to its proximity to the river and urban blocks, this location is a suitable option for the construction of public parks. Fourth case; it is located in the southern part of the region. This location is one of the best locations for park construction due to its proximity to different land uses. The proposed method and results could provide valuable information to managers when locating urban parks in Samawah City.}, Keywords = {Optimal Locating, Urban Parks, Geo-spatial Information Systems (GIS), Optimization, Genetic Algorithm}, volume = {10}, Number = {4}, pages = {35-48}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-982-en.html}, eprint = {http://jgst.issgeac.ir/article-1-982-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Nekouzadechaharmahali, E.}, title = {A Recursive Algorithm to Determining Lagrange Basis Polynomial Using Chebyshev Nodes}, abstract ={Interpolation is the process of estimating unknown values that are located between known values. Usually this process is done using different kinds of continuous functions. One of the most common types of continuous functions which can be used for interpolation, are polynomials. In approximation theory polynomial interpolation is utilized to approximating a complex function using a polynomial. In this issue polynomial coefficients can be determined using different computing methods. The basic procedure to determining the coefficients, is solution of vandermonde system. The system however, has only theoretical significance, since its solution by numerical methods is ill-advised on all counts (computational effort, storage requirement, accuracy). This is the reason to using some alternative methods such as Newton and Lagrange. These methods are two well-known representations of the unique interpolation polynomial. Newton representation is based on determining divided differences, while the other one is a very elegant alternative representation of Newtons general formula that does not require the computation of finite or divided differences. Lagrange representation can be utilized for any sets of interpolating points. In some cases Lagrange representation is used for interpolating between equidistant nodes. For example in GNSS positioning it is a common issue to find the satellites position using the coordinates are given in 15 minutes constant interval. generally computing Lagrange basis polynomial using current method requires O(n2) operations. So when we use polynomials with high degrees for interpolation, we expect a significant increase of computational effort. According to this issue in the last article we introduced a recursive algorithm to obtaining Lagrange coefficients using equidistant nodes. By the use of this algorithm, we had a significant improvement in computations speed. Despite the usage of equidistant interpolation, it is not a good idea to use evenly spaced points to approximating a function. Because in such a situation interpolated polynomial has wild oscillations near the edges of interpolation interval and does not converge to the main function, specially in high order polynomials. This nonconvergence is called Runge phenomenon. To avoiding this problem, other sets of interpolating points should be used, with more density at the end points of interval. The simplest examples of such a point sets, are the families of Chebyshev points. These points are set of zeros of the Chebyshev polynomial. By using Chebyshev nodes, interpolation will be more accurate. Since unwanted oscillations are gone. Due to the mentioned advantages of Chebyshev nodes, in this paper we are going to introduce a recursive algorithm  to obtaining Lagrange coefficients using these sets of points. computing Lagrange basis polynomial using this method requires O(n) operations unlike the current method. So by the use of recursive algorithm, we expect speed increase in computations process. To investigating this issue we obtained Lagrange basis polynomial for all integer numbers within [1,1000] interval. All of coefficients were computed for different polynomial degrees from 1 to 10 using MATLAB. In the following we recorded calculating times for both of computing algorithms and also for different polynomial degrees. After checking computing times we found a significant increase in processing speed by the use of recursive method. Although this method reduces processing time for all polynomial degrees, it is more effective when we use polynomials with high degrees. In other words when we use a polynomial with degree of one, the recursive algorithm is 1/3 times faster in comparison with usual algorithm; But when we use a polynomial in degree of 10, it is 3 times faster. So we conclude that it is logical to use this algorithm specially when we use high degree polynomials for interpolation.}, Keywords = {Approximation Theory, Lagrange Interpolation, Recursive Computation of Lagrange Basis Polynomial, Chebyshev Nodes, Equidistant Nodes}, volume = {10}, Number = {4}, pages = {49-56}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-960-en.html}, eprint = {http://jgst.issgeac.ir/article-1-960-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {HashemiDareBadami, S. and OmidiPour, M. and JelokhaniNiaraki, M. R. and Mahmoudi, S.}, title = {Development of a VGI-based Participatory Spatial Decision-making System to Distribute Relief Aids in the Event of Natural Disasters}, abstract ={One of the inseparable parts of human life in all parts of the world and time periods is the occurrence of various natural disasters such as floods, earthquakes, etc. Some countries, such as Iran, are more vulnerable to these events due to their geographical location and special climate conditions. Disaster management is important in the event of such disasters, which can reduce the loss of life, property, and mental health and prevent the imposition of exorbitant costs on the country. One of the most important aspects of disaster management is the proper and equitable distribution of the aid. The disadvantages of conventional and non-systemic approaches are lack of accurate information on the position of the affected areas and the type of needs of the people living there, inconsistency in the allocation of aid between the different organisations, and lack of awareness of the type and amount of aid allocated. Such a distribution causes problems, such as the unfair distribution of aid in the form of accumulation and loss of certain items in some areas, and the scarcity and high cost of certain items in others. A spatial decision-making system based on volunteered geographic information was proposed and implemented in this study in order to properly and fairly distribute aid in the event of natural disasters, which supports the disaster managers’ needs and functions. The proposed framework consists of two main phases. In the first phase three groups of users collect data on the affected areas and their needs in the context of volunteered geographic information.This data includes location data and the needs of the affected areas. After collecting a huge amount of data, in the second phase, with the help of multi-criteria decision analysis tools, the affected areas are prioritized in terms of need for assistance and items. Therefore management and monitoring of the process of distribution of items and allocation of aid to different affected areas is done. The advantages of using the proposed system are accelerating the identification of damaged areas, timely and targeted distribution of aid in a manner appropriate to the type and amount of needs, effective participation of injured and relieved people, prevention of loss of aid and possible abuses and saving time and money.}, Keywords = {Disaster Management, GIS, Volunteered Geographic Information, Spatial Decision Making System}, volume = {10}, Number = {4}, pages = {57-71}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-961-en.html}, eprint = {http://jgst.issgeac.ir/article-1-961-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Naeimi, Y. and Voosoghi, B.}, title = {A Modified Weighted Total Least Squares with Application in RAIM Algorithm}, abstract ={In this paper, first the method of solving the linear weighted total least squares, and then its generalization to nonlinear state is discussed; as the problem-solving model for determining the coordinates with pseudo-range GPS observations is fully consistent with this model. Available techniques for solving the TLS are based on the SVD and have a high computational burden. Furthermore, the other presented methods that do not use SVD, need large matrices, and there is need for placing zero in the covariance matrix of the design matrix, corresponding to the errorless columns, which increases the matrix size and, as a result, raises the volume of the calculations. But in the proposed method, problem-solving is done without need for SVD, without introducing Lagrange multipliers, and avoiding the error-free introducing of some columns of the design matrix by entering zero in the covariance matrix of the design matrix. It is performed only using easy equations and on the basis of summation principles, which results in less computing effort and high speed. In the following, an optimal method for weighting the design matrix is presented, which can yield a much better answer to the unknowns in the presence of many failures (here, up to three failures are assumed and tested). Besides, it can estimate the residuals vector so that the failure observations would have larger magnitudes than the others, and could help with detecting them in a safer and more feasible way with respect to any other method.}, Keywords = {GPS, RAIM, Weighted Total Least Squares, Optimal Weights.}, volume = {10}, Number = {4}, pages = {73-85}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-893-en.html}, eprint = {http://jgst.issgeac.ir/article-1-893-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Ahmadi, D. and AlSheikh, A. A.}, title = {Identification and Evaluation of Meeting Points for Ride-sharing Services Using Fuzzy Logic}, abstract ={Today, in big cities and in the road network, traffic congestion and its management has become a major problem, which results in this traffic congestion in addition to the mental and physical problems it creates for citizens, nothing but increased fuel consumption, increased air pollution and waste. Gone is not time and energy. One of the emerging solutions for traffic and transportation management is the discussion of ride sharing, in which the driver shares the empty seats in his vehicle with people who share a travel and time plan with him, which in addition to reducing The cost and energy that follows for both the passenger and the driver will increase the efficiency of the transportation network and reduce the traffic and the resulting problems. One of the most important issues in shared passenger services is the discussion of meeting points. This is important because in shared passenger services, drivers are looking to reduce the distance traveled and fuel consumption to reach the passenger, from On the other hand, each passenger has a specific time window and many of them want to walk a distance to reach a safe and comfortable place. Since the variables of this research, which include: slope, speed of the passage, width of the passage, distance to the intersection, distance of the passenger and the amount of deviation from the driverchr('39')s path, the variables are continuous, linguistic and inaccurate and is a continuous system. , The use of fuzzy logic is suggested. Therefore, in this study, by combining fuzzy logic and spatial information systems, a solution to identify and evaluate meeting points has been presented. In this study, the study area is the metropolis of Tehran. The results of this study indicate that with increasing the number of available meeting points, the number of suitable meeting points and adaptation rate increases by 15% and also the rate of deviation from the main route for the driver is approximately 14% and the distance traveled by the passenger to reach the point. Meetings are reduced by up to 40%.}, Keywords = {Ride Sharing, Fuzzy Logic, Meeting Points, Intelligent Transportation}, volume = {10}, Number = {4}, pages = {87-101}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-989-en.html}, eprint = {http://jgst.issgeac.ir/article-1-989-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Tayfehrostami, A. and AzmoudehArdalan, A. R. and Roohi, Sh. and Pourmina, A. H.}, title = {Dams Surface Area Monitoring from VV and VH Polarization of Sentinel-1 Mission SAR Images (Case study: Doroudzan Dam, Shiraz, Iran)}, abstract ={Dams as important man-made structures need to be monitored continuously and precisely. Variations in water surface area play an important role in this major. SAR images of the Sentinel-1 mission have been considered a promising tool to monitor water dynamics due to their cloud-proof, illumination-independent, and high spatiotemporal resolution properties. In this study, to monitor the surface area of the Doroudzan dam reservoir, SAR images of Sentinel-1A mission in two polarization (VV and VH) in 2018 and 2019 are used. After image pre-processing, images are classified into two-class, i.e. water and non-water, based on the thresholding method, and corresponding threshold values are selected from the image. Next, the area of the water body was computed. Then, the time series of the surface area of the dam reservoir is obtained from VV and VH polarizations and was compared with the time series obtained from in-situ areas computation of the Doroudzan dam reservoir. The results showed that: (1) The threshold values for the classification of images and water separation from non-water for VH from -21.36 to -23.01 dB and VV from -13.47 to -19.08 dB. (2) VV polarization with relative RMSE of %5.83 and correlation coefficient of %97.55 compared to in-situ surface areas achieved higher accuracy as compared to VH polarization which resulted in relative RMSE value of %9.21 and correlation coefficient of %83.63 as compared to in-situ areas; VV is more sensitive to water cover than VH and is more stable under seasonal variations than VH. (3) The surface area of the Doroudzan dam reservoir area had started to increase in February 2018, and then declined in May 2018; It also started to rise again in December 2018 and then declined in May 2019. The surface area obtained from VV polarization of the Doroudzan dam reservoir was at its maximum on 20 April 2019 with 43.6187 km2 and a minimum on 28 September 2018 with 24.2241 km2.}, Keywords = {Doroudzan Dam, Sentinel-1 SAR Images, VH and VV Polarization, Surface Area.}, volume = {10}, Number = {4}, pages = {103-116}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-988-en.html}, eprint = {http://jgst.issgeac.ir/article-1-988-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Davtalab, M. and Alsheikh, A. A.}, title = {Points Of Interest Recommendation Using Hypergraph on Location-based Social Networks}, abstract ={Point of interest (POI) is one of the important applications of location-based social networks (LBSNs) for users and business managers. LBSNs include various complex relations (i.e., POI-POI, user-user, user-POI, and so on), that more accurate modeling of them can lead to making a better recommendation. Since some relations are much more sophisticated than pairwise relations, and thus cannot be simply modeled by a graph. This study proposes a model for calculating the similarity of POIs and users based on hypergraph structure and by integrating that into the collaborative filtering (CF) method it can improve the recommendation performance. The results obtained from the real data set, Foursquare, show that the proposed model performs better than state-of-the-art methods in terms of accuracy. Taking high-order relations between POIs and users into account can improve recommendation performance by 2.7% in terms of accuracy. By integrating the proposed similarity learning into the collaborative filtering (CF) method, our method obtained approximately 33% improvements in accuracy compared to the traditional similarity learning methods.}, Keywords = {Points of Interest, Location-based Social Networks, Collaborative Filtering (CF),Hypergraph}, volume = {10}, Number = {4}, pages = {117-127}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-997-en.html}, eprint = {http://jgst.issgeac.ir/article-1-997-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Tashayo, B.}, title = {Fusing Global Digital Elevation Models Using a Combination of Geographically Weighted Regression Model and Particle Swarm Optimization Algorithm}, abstract ={Global Digital Elevation Models (GDEMs) are one of the most important sources of elevation data. In recent years, GDEMs have become increasingly popular with researchers due to their global coverage and free accessibility. The most commonly used GDEMs are AW3D, ASTER, and SRTM. Each of these models is produced by different technologies and have different strengths and weaknesses. This issue indicates that these data are not necessarily consistent with another, and their accuracy is dependent on the local topography of the earth. The main objective of this research is to fuse global digital elevation models to produce a model with higher vertical accuracy. In this regard, in this study, a two-step approach is proposed for fusing GDEMs. In the first step, a Geographically Weighted Regression (GWR) model is used to determine areas of the Earthchr('39')s surface that have similar properties. In other words, using the GWR model, regions of the study areas with similar behaviors are classified into the same classes. At this step, each of these study areas is classified into three, five, and seven classes. Among these modes, for both study areas, the best results are for five Class mode. In the second step, to fuse GDEMs, the optimum weight of each class defined for each of AW3D, ASTER, and SRTM models are estimated using the particle swarm optimization (PSO) algorithm. In order to evaluate the accuracy of the proposed method, it has been used to produce the fused DEM for two study areas of BumeHen and TazehAbad. In the first case study (BumeHen), the amount of Root Mean Square Error (RMSE) on test points in five class mode for AW3D, ASTER and SRTM are 4.58, 8.69 and  4.70 meters respectively, while it’s 3.97 meters for fused DEM. In the second case study (TazehAbad), the amount of RMSE on test points in five class mode for AW3D, ASTER, and SRTM are 3.33, 7.31, and 3.17 meter respectively and it’s 2.74 meters for fused DEM. The results show that the proposed method is capable of producing a higher accuracy model than any of the initial models by utilizing the potential of each of these input models in the fusion process.}, Keywords = {Digital Elevation Models (DEMs), Particle Swarm Optimization (PSO) Algorithm, Geographically Weighted Regression (GWR), Fusion of Elevation Data}, volume = {10}, Number = {4}, pages = {129-142}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-930-en.html}, eprint = {http://jgst.issgeac.ir/article-1-930-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Sadeghi, V. and Ebadi, H. and Sadeghi, V. and Moghimi, A.}, title = {Automatic Land Use/Land Cover Change Detection from Multitemporal Remote Sensed Images and Old Maps by Refining of Training Data Based on Chi-Square Test and K-Means Clustering}, abstract ={The training data selection is an important and operative step in the classification and change detection procedure from remote sensing images, which needs to be provided with high sensitivity. These samples are often determined by the human factor, which is a time-consuming process and prone to high error. Old maps can be a valuable source of information for selecting and preparing training samples. If these samples are accurately refined, they can speed up, facilitate and also increase the accuracy of the change detection process. The main innovation of the present paper is the diligence in the sampling process, which has been made imaginable by proposing a model based on the chi-squared statistical test and k-means clustering. This method, while using Chi-square statistical test, tries to select pure training samples, by selecting samples that are close to the centers of internal clusters in each class with multiple k-means clustering that takes into account the internal spectral diversity of classes. In this method, the spectral and the first and second-order of co-occurrence matrix are extracted and used in the support vector machine (SVM) classification process. Furthermore, the feature selection and SVM parameters have been optimized by the genetic algorithm to more improve the classification and change detection accuracy. For implementation, bitemporal satellite images at 2011 and 2015 and land use map of 2009 related to the Shiraz has been employed. Using the proposed method led to update the thematic maps of the study area with an overall accuracy of 98.72% and 94.57%, and a from-to change map. Experimental results showed that the refinement process of the training samples improves the results of the 2011 image classification (increasing the kappa coefficient from 65% to 87% and increasing the overall accuracy from 73% to 91%) as well as the 2015 image (increasing the overall accuracy from 69% to 86.32% and Kappa coefficient has been increased from 59% to 80.48%).}, Keywords = {Change Detection,Updating, Refinement of Training Data, Chi-square Test, k-means Clustering}, volume = {10}, Number = {4}, pages = {143-161}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-935-en.html}, eprint = {http://jgst.issgeac.ir/article-1-935-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Torabi, M. and Hosseinali, F. and Ghiasvand, H.}, title = {Developing an Agent-based Simulator Model to Improve Cities\' Traffic Flow}, abstract ={Nowadays, cities’ population has been encountered with rapid growth. However, the urban transport infrastructures are not developed with this rate. This issue yield to the common problem which is the traffic jam in streets. The practical approach to solve this problem is the efficient use of the infrastructure and proper traffic control which plays an important role in this performance. Based on the aforementioned issues, the aim of this study is an urban traffic control system using the agent-based simulation approach. In this study, the microscopic model for efficient network traffic junctions in the context of the flow of traffic at intersections has been used. The proposed approach, by defining the main parameters of traffic as a collection of micro-scale smart agents, is expected to address the issue mentioned more effectively. These agents consist of three categories (vehicles, traffic control centers and traffic lights) with different functions. In our approach in addition to the defined scenarios for intersection with a focus on optimizing time, total optimization of traffic flow will also be followed. Also, path finding is used to test the performance of the model. Having the traffic lights with three phase and two methods of path finding, six scenarios are defined and are implemented in a simulated environment. The results of the comparison parameter stop time and the average speed of vehicles reveals the fact that using the intelligent path finding and smart traffic light (sixth scenario) would lead to the downturn about 3.12 seconds per kilometer for stop time and upturn near 1.83 km/h for average speed of cars using the proposed method. We used the controlled data for the evaluation process and the relative accuracy was 83% for stop time and 94% for average speed of vehicles. The results of this study revealed the efficiency as well as reliability of the developed agent-based model in traffic smoothing.  }, Keywords = {Agent-based Modeling, Intelligent Routing, Traffic Control Systems, Traffic Light Agents}, volume = {10}, Number = {4}, pages = {163-177}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1001-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1001-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {HeidariMozaffar, M. and SalehJoneghani, E.}, title = {Modeling Urban Expansion and Development of Isfahan City by Using Remote Sensing Data in LCM Model}, abstract ={      urban population, especially in developing countries, is increasing gradually. Optimal use of land plays an important role in planning and urban development management. This principle is especially important in achieving sustainable development in urban areas. Not-principles Exploitation causing destruction and degradation of resources. Study dynamics of land use changes and their broad impacts on the environment, it is essential to understanding how these changes occur, both in terms of spatial pattern and in terms of its quantity. In this article, it was noticed to simultaneously apply spatial information and remote sensing information to study and model land use change in order to Isfahan city. land use maps using Landsat images for the years 1997, 2008 and 2017 manufacturing and maps of 1: 25000 scalewere used to better identify the area, land reference and geometric correction of satellite images. Transmission potential modeling using perceptron multi-layered artificial neural network algorithm was down. Through certain dynamic variables include distance from academic centers, distance from industrial centers, distance from residential areas, distance from passages as well as digital elevation modelas a static variable; then, The amount of each land use conversion is predicted with the Markov chain and the total map of land use change made with two models of hard prediction and soft prediction in the LCM model.The results ofland use transformation potential modeling in all submodels showed over 95% accuracy. the calculation of accuracy of the prediction model, i.e. the kappa coefficient equal to 0.9 was obtained. The results obtained from the study of changes and prediction of land use indicate the development of urban areas Isfahan. These changes also indicate that decrease of other land use classes. According to the results of the LCM model over the whole span, the urban lands have increased from 21239 hectares in 1997 to 23607 hectares in 2017; this upward trend will continue into the future. In modeling and according to the results of the Markov chain model, the urban lands will reach 24023 hectares by 2027.}, Keywords = {Isfahan City, Landsat Images, Land Use, LCM Model, Markov Chain, Perception Neural Network}, volume = {10}, Number = {4}, pages = {179-190}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-922-en.html}, eprint = {http://jgst.issgeac.ir/article-1-922-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Fakhri, S. A. and Saadatseresht, M. and Varshosaz, M. and Zakeri, H.}, title = {Automatic Estimation of the Spatial Resolution Coefficient of UAV Images Based on Siemens Star Target}, abstract ={In recent years, the use of unmanned aerial vehicles (UAVs) that has been introduced to UAVs has been widely used in surveying engineering under the name of UAV photogrammetry. Different urban and non-urban scales on different scales provide the conditions for examining and evaluating the geometric accuracy of these high-resolution spatial images in the production and delivery of large-scale maps and coverage scales. One of the most important geometric parameters in UAV images is the determination of the spatial resolution, which is known as the criterion for detecting the smallest distance between two adjacent objects that can be distinguished in the images. There are several ways to accurately measure the spatial resolution of images; in this study, the Siemens star was used as one of the most widely used artificial targets in measuring spatial segregation. The purpose of this paper is to provide an automatic method for detecting the radius of ambiguity and calculating the spatial resolution limit in images taken from UAVs. The results of this study showed that firstly, according to the flight altitude and the amount of blurring of the image, the Siemens Star Target should be used with appropriate dimensions and number of arms, and secondly, the rate of reduction of image resolution in the tested drones was between 1.2 and 3.7.}, Keywords = {UAV, Spatial Resolution, Siemens Star}, volume = {10}, Number = {4}, pages = {191-204}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-949-en.html}, eprint = {http://jgst.issgeac.ir/article-1-949-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} }