@article{ author = {Alipour, S. and Alesheikh, A. A.}, title = {Epidemiology and Ecological Study of FMD Based on Spatial Analysis (Case Study: Iran)}, abstract ={Foot-and-Mouth (FMD) is a viral and contagious disease that endangers both domestic and wild animals and this disease has caused a lot of social and economic damage to the livestock industry in Iran. Considering the endemism of this disease in Iran, the main purpose of this study is to investigate the temporal trend, discover spatial and spatio-temporal clusters and determine the impact of environmental factors and model the disease in order to better understand the spatio-temporal epidemiology of FMD in Iran. In this study, data related to the incidence of disease as well as environmental variables including meteorological variables, vegetation and topography of the region were used. This study was performed on 12442 cases of registered diseases in the years 1396-1387 in the city for the whole of Iran. First, general auto correlation indices including Moran'I index, General G index and Ripley's K index were used to investigate the distribution of the spatial pattern of the disease. Local Moran and the Gatis-Ord G* index were used to detect hot and cold spots. Then, with the help of the points of occurrence and time of the disease, spatial and spatio-temporal clusters of the disease were discovered. Spearman's correlation and linear regression analysis were used to evaluate the type and severity of correlation between environmental parameters and disease incidence. The results showed that the maximum incidence is in spring (44%) and the minimum incidence is in summer (15%). The months of Ordibehesht, Farvardin, Khordad and Dey, with a total of 6650 cases, were the most riskful months of the year, respectively. Autocorrelation tests showed the distribution of FMD clusters for every 10 years studied. The results of the local Moran index and the Gatis-Ord G* showed that the foci of this disease are still active in Iran and parts of Semnan, Tehran, Markazi, Fars, Lorestan, Khorasan Razavi and East Azerbaijan provinces are hot spots of FMD. In spatial scan, 8 clusters were identified, which confirmed the results of Moran and the Gatis-Ord G* index. In the spatio-temporal scan, 5 clusters were discovered that were consistent with the results of the spatial scan. Spearman analysis showed that there is a positive correlation between the incidence of the disease and vegetation, which is decreasing from the beginning to the end of the year and has a negative relationship in winter. Also, a positive and increasing correlation was observed with wind speed and precipitation from the beginning to the end of the year. In addition, a positive correlation between the incidence of the disease and altitude and a negative relationship with the direction of the slope was calculated. Linear regression analysis was also used to evaluate the effect of variables, which resulted in confirming Spearman correlation outputs. The results of the model showed that vegetation and topography are the most important environmental factors that affect the prevalence of snow fever in the region. The results of this study identify areas that are at higher risk and need more planning and attention to control the disease.}, Keywords = {Foot-and-mouth Disease, Geospatial Information System, Moran Index, Spatiotemporal Scan Statistics, Spearman Correlation}, volume = {11}, Number = {1}, pages = {1-17}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-957-en.html}, eprint = {http://jgst.issgeac.ir/article-1-957-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Dehghani, S. and Alesheikh, A. A.}, title = {Discovering and Analyzing Regions of Interest Based on Geo-Tagged Images}, abstract ={The deployment of new technologies in digital devices and communications has increased social networks' popularity and pervasiveness. Geo-tagged content has connected cyberspace to the real world by adding a new dimension to social networks. Among geo-tagged content available on social networks, geo-tagged images show users' communication and interaction with the environment in a better way. Because users frequently photograph regions of interest, these images can be used in many applications, including discovering regions of interest. Compared to traditional methods such as censuses and surveys, geo-tagged images benefit from saving time and expense to discover and analyze regions of interest. Therefore, researchers can use them in urban management and tourist recommendation. The purpose of this study is to discover the region of interest using geo-tagged data. Also, extracting appropriate semantic information and analysis in different contexts to identify regions of interest and understand the reason for their attractiveness is another goal of this research. This paper uses the Flickr geo-tagged images taken from New York City between 2015 and 2018. In the preprocessing phase, noise and data redundancy was removed. Then the data were clustered by the HDBSCAN method, and adjacent clusters that were similar in terms of text tags were merged. As a result, 106 regions of interest were identified. At the next step, a concave surface was fitted to points by α-shape method, and semantic information including distinguished labels, names, and categories, was selected for regions of interest. Finally, attractive regions were analyzed based on the type of visitors, users’ sentiment and the number of visits in different contexts. The evaluation results show the discovery of regions of interest in different shapes, dimensions, and densities. Our result corresponded for 66% with TripAdvisor's top attractions, while for the simple DBSCAN method this value was 53%. In regions that overlapped with TripAdvisor attractions, the naming was 76% similar.}, Keywords = {Region of Interest (ROI), Location-based Social Networks, Geo-tagged Images, Spatial Clustering, Flickr}, volume = {11}, Number = {1}, pages = {19-34}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1000-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1000-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Farhadi, N. and Kiani, A. and Ebadi, H.}, title = {Development of a Model based on Gradient Resonance in Deep Convolutional Networks to Identify Targets in Remote Sensing Images}, abstract ={Advances in Remote Sensing technologies provide various information regarding object detection problems. This information makes the interpretation of optical remote sensing images easier. Especial kinds of these interpretations relate to Object Detection approaches that most researches in this field are carried out using Neural Networks and Deep Learning techniques; Design of the network is an important process that affects detection accuracy. Recent researches in the deep learning field and convolutional neural networks show that deeper networks can achieve better accuracy. However, in previous researches, sometimes too deep networks are the reason for other problems such as increasing the number of trainable parameters, vanishing gradients, unused extracted features, etc. These problems decrease the accuracy of the network in recognition of objects. This issue has been mentioned in many types of researches in the field of convolutional networks, and they have tried to meet the challenge by examining different topologies or presenting new training methods. In this article, a model was developed and tried to keep extracted features and transfer them to the next layers. The proposed architecture is a combination of several blocks stacked in a row. The blocks receive their input from the previous block and perform the relevant calculations. Each block consists of several cells that have two layers of convolution. To efficiently use all the features of the training images, the filters used in the convolution layers have kernels with sizes of 1×1 and 3×3. The output of the 3×3 layer in the combining stage is integrated with the information of the previous layers. The architecture of each cell in the proposed network keeps all the extracted features from previous layers to be used in subsequent cells. With these connections between layers, the networks can be deeper with fewer effects of vanishing gradient. In addition to solving gradient problem, this architecture decreases the number of trainable parameters and duration of the training phase impressively. The result of this process is an increase in the ability of existing models to distinguish multi-class objectives. For this purpose, first, a collection of 320 training images is proposed and preprocessed. The proposed method is defined as feature extractor of Faster R-CNN model, and it is trained on image collection. To evaluate the proposed method, a part of Beijing International Airport and a part of Imam Khomeini International Airport were selected as the first and second case study areas. The F1-Measure criterion values for both regions are 97.9 and 93.7, respectively. While, ResNet architecture with 101 layers of convolution and 14.4 million more trainable parameters than the proposed architecture has achieved values of 96.7 and 93% for the mentioned criterion. Finally, the results of applying the proposed model were compared with different famous models of the existing network. The experimental results indicated the reliability and efficiency of the proposed method. To improve the proposed architecture in this paper, dilated convolution operators can be used to extract more prominent features. On the other hand, with the aim of development and generalization, the proposed method can be applied in two stages on high resolution remote sensing images; In the first step, the goal is to identify the location of the airport, and in the next step, the planes inside each airport will be identified by the proposed method.}, Keywords = {Deep Learning, Convolution Networks, Remote Sensing Imagery, Object Detection, Feature Extractor, Artificial Intelligence}, volume = {11}, Number = {1}, pages = {35-50}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-871-en.html}, eprint = {http://jgst.issgeac.ir/article-1-871-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Mohsenifar, A. and Sedaghat, A. and Mohammadzadeh, A.}, title = {An Integrated Satellite Stereo Image Registration Method Based on KAZE, VFC and TPS Algorithms}, abstract ={Image registration is the technique of aligning two or more images with a reference image all acquired at the same geographical area with different acquisition dates, viewpoints, and imaging sensors. This technique is a fundamental process in photogrammetry and remote sensing tasks, and change detection is known as one of its important applications. Urban change detection with an arbitrary time difference between the two images faces limited access to the required datasets. The Google Earth software is known as an appropriate resource to achieve such datasets. Nevertheless, a significant limitation of this software is the co-registration error of the images acquired at different times. In this work, a combined image registration approach has been proposed to cope with this problem. The proposed method is made up of three major steps. In the first step, the KAZE algorithm is applied to extract and describe image features with high stability against illumination distortions. In the second step, the image matching and mismatch elimination procedures are conducted using the VFC (Vector Field Consensus) technique. Finally, in the third step, the geometric relation between the two images is established using the TPS (Thin-Plate Spline) adaptive transformation model. The results derived from the KAZE feature extraction and matching algorithm are compared with those of the SIFT and SURF algorithms in four satellite image pairs related to various urban areas. For datasets 1 to 4, the highest correct correspondence rates of 0.63, 0.81, 0.6, and 0.76 were obtained using the KAZE algorithm compared to the other algorithms. Moreover, the TPS transformation model established the most accurate and reliable geometric relation between the two images with RMSE values of 2.1, 1.8, 2.1, and 1.7 pixels for datasets 1 to 4, respectively. Accordingly, the results indicate the efficacy of the proposed integrated method for robust feature extraction and image registration of the Google Earth imagery with significant brightness and landscape differences.}, Keywords = {Image Registration, Google Earth, Control Points, KAZE, VFC, TPS}, volume = {11}, Number = {1}, pages = {51-69}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-968-en.html}, eprint = {http://jgst.issgeac.ir/article-1-968-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Hosseini, M. S. and Azizi, Z. and Vafaeinejad, A. R.}, title = {Monitoring the Changes of Govater Estuary Mangroves Using Airborne-Space Imagery}, abstract ={Mangrove forests are a collection of trees and shrubs that are located in the tropics and this tidal plays an effective role in their growth, establishment and continuity of life. Mangrove ecosystems are found in tropical and subtropical regions of the world. Mangrove forests may exist in wide ranges between latitudes 35 ° N and 38 ° S. Mangroves are one of the most globally adaptable conservation ecosystems on the planet, providing invaluable services to coastal areas and the millions of people living in these communities, but because most of these services cannot be priced at market prices. As a result, the importance of these unique ecosystems is often underestimated. Due to the environmental role of mangrove forests, monitoring the changes in these forests is of particular importance. However, the location of these forests between water and land has made the direct study of this phenomenon difficult. In recent years, increasing attention to remote sensing science has removed barriers to access to these areas and made it possible to study these plants in any regional conditions. For this purpose, in this study, using telemetry data, the changes of Goiter estuary in the southeast of Sistan and Baluchestan province in a period of 30 years were investigated and Landsat satellite imagery (TM, ETM + and OLI sensors) from 1988, 1996, 2005, 2013 and 2018 were used. Developments were examined in the form of four study periods: 1988-1996, 1996-2005, 2005-2013 and 2013-2018. In order to calculate the amount of mangroves in this area each year and prepare a map of the distribution of mangrove masses, the Normalized Difference Vegetation Index was used. In the mentioned environmental conditions, only mangrove species are able to survive and the presence of other plant species in such conditions is far from the mind and as a result, this index can have a good performance in the field of mangrove studies. The study of mangrove values in the mentioned years showed a positive growth of mangrove values in the first three study periods. In the first period 32.31 hectares, in the second period 59.31 hectares and in the third period 1.17 hectares was added to the area of mangroves. This increase has more than doubled from 1988 to 2013. But from 2013 to 2018, there was a sudden decrease of 59.94. This reduction in area in tropical forests can be affected by various reasons. In this project, according to the comparison of the spectral diagrams of mangroves and the marginal waters of this cover, it seems that this reduction is false and is affected by the tidal phenomenon. The spectral behavior of two ranges proved that despite the differences between the two curves in reflection, their overall structure is the same and the red curve is a combination of water reflection and vegetation, indicating the presence of mangroves underwater when Is fashion. Therefore, it can be said that the decrease in 2018 was due to the placement of part of the mangroves under water and therefore there was no decrease in the area.}, Keywords = {Mangrove Forests, Remote Sensing, Change Monitoring, Landsat Satellite, Tidal}, volume = {11}, Number = {1}, pages = {71-78}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-998-en.html}, eprint = {http://jgst.issgeac.ir/article-1-998-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Jelokhani-Niaraki, M. R. and Rahmani, M. and Kiavarz, M.}, title = {Evaluation of Citizens’ Efforts in Participatory Production of Spatial Data}, abstract ={Recent developments in information and communication technologies, including the emergence of the Internet and the Web as a global infrastructure, have provided valuable opportunities for the active participation of ordinary people in the planning, decision-making, development and advancement of science and knowledge. The web and mobile GIS technologies have provided the necessary platform for people to actively participate in the production of spatial data by providing the ability to draw on the maps via smartphones equipped with GPS systems. Unlike the traditional process of data production, which is closed, expert-centered, and organizational, participatory spatial data is generated by each user and made available to others for free. In other words, each person is both a consumer and a producer of spatial data. The amount of effort made by people trying to produce spatial data is directly related to the quality of the spatial data produced. The low effort of a user in the production of spatial data, indicates that the user produces data with low quality or, conversely, users with high interest and motivation who spend more effort to produce data, improves data quality. Therefore, it is necessary for users of participatory spatial data (such as government organizations) to be aware of the amount of effort made in the production of spatial data. In other words, by measuring and evaluating the amount of effort made in data production, the quality of data can be ensured to some extent. Therefore, determining and evaluating various indicators that indicate the amount of effort made in the production of spatial data is an important and necessary issue. The purpose of this study is to investigate and evaluate the efforts of citizens in producing spatial data related to urban problems. For this purpose, five indicators including the time spent drawing features, the number of zooming performed, the number of features drawn, the difference between the number of complex and simple features and the number of spatial edits on the map were selected. To measure the above indicators, a participatory web and mobile GIS was implemented in District 6 of Tehran. The results show that most users spend about 16 to 32 seconds between drawing the first and last feature, 0 to 3 times zooming operation, 0 to 2 times drawing the feature and 0 to 3 times the activity of restoring the drawn feature to the previous state and redrawing (Undo-Redo) on the map. In addition, the number of polygon features drawn by most citizens is more than point features (high effort) while for 17 people the opposite is true (low effort). In general, the low number of the above activities shows that citizens have not spent much effort and accuracy in producing spatial data. It is suggested that future studies examine and evaluate the relationship between user characteristics and indicators of effort in producing spatial data. Moreover, the amount of effort of users in producing spatial data related to a geographical phenomenon is affected by the degree of complexity, user perception and knowledge about that phenomenon. Drawing some phenomena requires more thought, time and effort. For example, drawing a contaminated area whose boundary is not precisely defined is far more complex than drawing an urban green space that has a clear boundary. Therefore, it is suggested that future studies examine and analyze the relationship between user perception and understanding of a spatial phenomenon and the amount of effort in producing data of that phenomenon.}, Keywords = {Participatory Spatial Data, Citizen-centered GIS, Mapping Effort}, volume = {11}, Number = {1}, pages = {79-90}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1022-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1022-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {GhorbanalizadehKhangah, M. and Tehranchi, R. and MoghtasedAzar, Kh.}, title = {Noise Analysis of GPS Time Series with Simulated Data Using EMD, Standard Deviation and Trigonometric Methods}, abstract ={Due to the wide application of GPS time series such as the study of tectonic movements, land crust change and earthquake dynamics, etc., it is important to provide a method to increase the speed in the analysis of variance components. Researchers have shown that nearly 90% of GPS time series have a combination of white+flickr noise and a lower percentage have a combination of white+random-walk noise. Researchers have used a trigonometric relationship inside the maximum likelihood estimation (MLE) method, to reduce the dimensions; instead of calculating 3 unknowns (white, flicker and random-walk noise components) they calculated 2 unknowns, a phase such as phi introducing relationship between white noise and colored noise and a phase such as theta introducing relationship between two colored noise (flicker and random-walk noises). On the other hand, researchers have been able to estimate white noise by empirical mode decomposition (EMD). In this research first we try to estimate the white noise using EMD method and then with the mentioned trigonometric relation, we can estimate the flicker or random-walk noise. It is expected that if this project is successful, the noise components will be estimated immediately. Finally, the results of the proposed method are compared to the results of least square variance estimation (LS-VCE) method. In another new method, since white noise can be extracted and separated by EMD, first white noise is extracted and then GPS time series components including linear trend, periodic movements with annual and semi-annual frequencies are extracted by least squares. The type of colored noise (flicker or random noise) can be determined by the Hurst parameter and assuming that the residual is flicker or random-walk noise, the statistical information of colored noise can be estimated (standard deviation method). The methods were first tested on simulated series and after its success, real GPS time series were used for verification. In this study, extracting white noise by EMD has been fundamentally reviewed; including how to decompose and how to detect white noise through intrinsic mode functions (IMFs) by Hurst parameter. Among Hurst parameter estimation methods, 12 methods were evaluated and boxed-periodogram method had better results. The results of the new methods are very efficient for simulated and GPS time series with white+flicker noise, but challenging for series with white+random-walk noise.}, Keywords = {GPS Time Series, LS-VCE, EMD, Hurst Estimation, Fast Noise Analysis}, volume = {11}, Number = {1}, pages = {91-106}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-985-en.html}, eprint = {http://jgst.issgeac.ir/article-1-985-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Rasoulinia, M. and Sharifi, A.}, title = {Potato yield estimation using Sentinel-2 satellite images, Case study: Sarab city}, abstract ={In recent years, the world's population has been growing, reaching about 7.7 billion in 2018. In the next five years, the world's population will increase to about ten billion, thus increasing the demand for food, water resources and fertile land for food production. Therefore, precision agriculture is trying to improve the quantity and quality of agricultural products by using new technologies. Precision farming is a new concept in modern agriculture and is based on the existence of heterogeneity at the farm level. One of the reasons for the growth of precision agriculture among scientists and farmers is the advancement of technology in various fields such as global coordinate system, imaging sensors and spatial information management tools. The use of remote sensing allows us to control a very large space in a very short time, which will reduce heavy costs. Along with rice and wheat, potatoes are the third most important food crop in the world, fed by more than one billion people, and the demand for this crop has increased as the population has grown; Therefore, better methods of product protection and management need to be used to improve production. In order to build the whole model, the images of the region were considered during the months of March to October in a period of two years, and pre-processing such as removing the cloud and other errors were done in order to have images with the least error. The NDVI index was then calculated using red and infrared bands (Figure 3). By calculating the NDVI index, useful information about the plant such as health, plant vigor, presence or absence of fall, and proper irrigation were obtained. On the other hand, land information, agricultural lands have been collected through questionnaires. For modeling, we deal with two sets of terrestrial and satellite data. Separate use of each of them will be associated with shortcomings and shortcomings; Therefore, for modeling, a combination of terrestrial and satellite information was used to build a model with minimal deficiencies and defects. It is common for modeling to work repetitively to obtain the best algorithm, so linear regression is the best choice for modeling. Univariate method was used for modeling and the date of 28 June was selected as the modeling interval. Because using multivariate methods, due to the fact that there was a high correlation in the information of different time periods, a lot of duplicate information was generated, and for this reason, we used the univariate method. Four machine learning algorithms were used for modeling, which Gaussian methods and support vector showed the best results. According to the production estimation model, it is possible to predict the amount of potatoes produced in each agricultural land and its health status with 50% accuracy three months before the harvest. The results of this study showed that estimation models will provide very valuable information for better management of crops to be used by agricultural planners to avoid wasting resources and prevent imports.}, Keywords = {Sentinel-2, Yield Estimation, Potato, Machine Learning Regression}, volume = {11}, Number = {1}, pages = {107-116}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1015-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1015-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Javadnia, E. and Beygi, N. and Abbasy, S.}, title = {Comparison and Evaluation of MODIS and Sun photometer Integrated water vapor using GPS observations at IASBS AERONET site (Zanjan, Iran)}, abstract ={Water vapor is the most important atmospheric greenhouse variable gas, both spatially and temporally. It involves a positive feedback loop in climate change. In addition, the atmospheric water vapor content is not only an important parameter to forecast precipitation and severe weather but also a central factor for studying the global water cycle. Therefore, in order to weather forecast and climate monitoring applications, study of spatio-temporal distribution of water vapor is valuable. Today, satellite sensors, due to their wide spectrum range, high -spatial resolution, and almost daily global coverage, have enabled them to observe the Earth's atmosphere and constantly monitor its changes. One of the atmospheric parameters measured by satellite sensors is water vapor. Among the various sensors that measure water vapor, the MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most famous. MODIS offers water vapor in the form of two separate products, one is the near-infrared product and the other is the infrared product. In addition to satellite sensors, water vapor can also be accessed through observations of ground-based devices such as Global Position System (GPS) and Sunphotometer. In this research, among the MODIS water products, the near-infrared product was used and the data measured at the Aerosol RObotic NETwork (AERONET) site along with the computed values from GPS observations were used to validate the satellite water vapor data. In this research, the method of Point Precise Positioning (PPP) has been used to process GPS observations. The main goal of this research is the investigation of MODIS and AERONET water vapor data quality using GPS observations at Zanjan city. In this study, standard MODIS near-infrared level-2 Integrated Water Vapor (IWV) data product and AERONET IWW retrievals from sunhpotometer at Institute for Advanced Studies in Basic Sciences (IASBS) site, were evaluated using estimated data from GPS permanent station of IASBS from 2011 to 2013. Intercomparison results showed that AERONET-IWV and GPS-IWV had high correlation (R2=0.95). The mean values of an Root Mean Square (RMSE) and Bias between AERONET-IWV and GPS-IWV (AERONET-GPS) were about 3.2 mm and 2.2 mm, respectively. The MODIS IWV product showed slightly higher correlation coefficient (R2 =0.92) with GPS-IWV compared with AERONET –IWV (R2 =0.90). In addition, MODIS-NIR IWV showed approximately 2 times larger RMSE and 2.5 times larger Bias from AERONET -IWV (RMSE =5.48 mm, Bias =4.88 mm) compared with GPS IWV (RMSE =2.76 mm, Bias =2 mm). Finally, the results of the presented study provide good information about the overall quality of the MODIS and AERONET IWV data for the areas of atmospheric researches. The results of this study were comparable to previous studies conducted in Kanpur India and Beijing, China. The results obtained in this research provide valuable information to researchers in the fields of meteorology, climate, hydrology.}, Keywords = {Water Vapor, MODIS, AERONET, GPS}, volume = {11}, Number = {1}, pages = {117-128}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1007-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1007-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Pourhasan, N. and Shah-Hosseini, R. and Seydi, S. T.}, title = {Deep Learning-based Classification Method for Crop Mapping Using Time Series Satellite Images}, abstract ={Awareness and knowledge about the cultivation pattern and the area under cultivation play an important role in agricultural land management and estimating net production. Combining the results of ground observations and measurements with remote sensing data can provide timely maps of crops. This is valuable for defining management units and achieving accurate information needed by farmers and planners. Most of the methods used to separate agricultural products do not work well when the cultivation pattern of different crops, such as wheat and barley, is very similar. Therefore, the purpose of this paper is to provide a deep learning-based classification method on satellite time-series images to produce a map of the exact area under the cultivation of different types of agricultural products with high technological similarity. For this purpose, the Landsat8 satellite time-series images were selected based on the region's crop calendar. Using a normalized vegetative differential indication index (NDVI) as a time series and a set of training data from various agricultural farms, the Convolutional Neural Network (CNN) was used to automatically generate a product map in the Chenaran region of North Khorasan Province. In order to separate the agricultural products in this study, a combination of supervised classification and visual correction has been used. In order to estimate the accuracy of the results, the maps produced with the ground control points were examined and the Kapa coefficient and overall accuracy were calculated. The results showed that the use of satellite series time data is highly effective in identifying and distinguishing different types of agricultural products. Also, the classification method based on the convolutional neural network with an overall accuracy of 95.76 has higher accuracy than other conventional methods such as random forest methods (overall accuracy: 89.85), backup vector machine (overall accuracy: 88.78), network Perspective neurons (overall accuracy: 85.75) and K have the closest neighbor (overall accuracy: 89.60) in separating and identifying agricultural products. The present study was conducted to investigate the performance of the CNN classification method in producing the map of the area under cultivation of agricultural products in Chenaran city using Landsat8 satellite time-series images. The results showed that the use of the proposed CNN-based classification algorithm in identifying agricultural products according to the same training samples and two quantitative accuracy evaluation criteria showed better performance than other methods used and was able to effectively overcome to the existing challenges. It is very important to have accurate maps of the area under cultivation of various agricultural products in each region because this knowledge can be used in various fields such as improving the cultivation pattern of agricultural products, planning in the field of water resources required by the agricultural sector, Estimate the required budget to be used to allocate the machinery needed for each section. Therefore, using this method with optimal cost and time to produce a crop map is recommended. In future research, a combination of radar and optical images of Sentinel 1 and 2 satellites and deep learning networks will be used to achieve higher accuracy and better spatial resolution maps of the area under cultivation.}, Keywords = {Crop Mapping, Remote Sensing, Landsat8, Normalized Difference Vegetation Index (NDVI), Covolutional Neural Network (CNN)}, volume = {11}, Number = {1}, pages = {129-142}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-938-en.html}, eprint = {http://jgst.issgeac.ir/article-1-938-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {AliAslKhiabani, E. and ValadanZoej, M. J. and MaghsoudiMehrani, Y.}, title = {Monitoring the Earth-fill Dams Displacement by Using the Time Series Radar Data}, abstract ={Today's cities are full of large structures that require very high cost and time to monitor in traditional ways. Existing engineering structures are deformed due to various factors such as landslides, land subsidence, earthquake, flood, explosion and etc. With increasing the number of large engineering structures in cities, experts are looking for a good solution for monitoring these structures to avoid great financial and human damages.  One of the most important huge structures in any country is the dam. In addition to providing drinking water and water for agriculture, dams prevent devastating floods and are able to generate a large amount of hydropower. Therefore, continuous monitoring and study of the displacement and deformation behavior of the dams is essential. From the past, leveling and ground surveying were carried out to measure the deformation of structures and ground displacements along the vertical direction; but these measurements are time-consuming and costly. Also, the use of precision instruments and deformation sensors are not suitable because of their high cost, time-consuming and complex. Due to the ability of Radar images and Radar interferometry techniques in the field of monitoring the ground displacement, in this research, we are looking for evaluating the potential of this method for monitoring the dam deformation and displacements. To achieve this goal, we used two sets of radar data which are CosmoSkyMed-X and Sentinel-1A.   The study area in this study is Daryan earth-fill dam which is located in Kermanshah province. Daryan Dam is a gravel dam with a clay core. The length of the crown of the dam is 368 meters, the width is 15 meters and the height of the foundation is 179 meters. The method used to estimate the displacement of the dam is the Persistent scatterers (PSI) technique. In the time series processing of these images, the PSI method was selected then the star graph and Deloney triangulation were used. In the next step, we used both linear and nonparametric models for displacement estimation. The results were evaluated by applying two different displacement models and finally, the model with higher temporal coherence was selected as the appropriate model and the other model was discarded. In processing the Daryan dam images, the appropriate model for monitoring the displacements with S-1A Radar data was the nonparametric model and for CSK data was linear model. The results of the accuracy assessment showed that by using CSK and S-1A radar data, we can monitor the earth-fill dam displacements by the precision of 2 and 4 mm which these precisions are acceptable and standard. It is important to note that the results of X-band data due to it's higher resolution, has a higher density of PS points, and higher accuracy. Also, the larger size or greater area of the dam body makes more the density of the PS points and the displacements obtained for these points will be more accurate. Generally, we can say that the X and C-band radar data have the potential to monitor the earth-fill dam displacements by radar interferometry technique and this method can replace with traditional and costly methods.}, Keywords = {Radar Interferometry, Persistent Scatterer Technique, Earth-fill Dam, CosmoSkyMed-X Images, Sentinel-1A Images}, volume = {11}, Number = {1}, pages = {143-160}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-925-en.html}, eprint = {http://jgst.issgeac.ir/article-1-925-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Bararisiavashkolaei, M. and Sharifi, A. R. and KarimaeiTbarestani, M.}, title = {Analysis of the Efficiency of Remote Sensing Data for Estimation of Water Balance Components (Case Study: Tajan Catchment, Mazandaran Province)}, abstract ={Water reserves are considered as renewable resources and the process of renewal is due to the water cycle in nature. However, the amount of water that emerges in this way on the surface of the earth or in any given geographical area is fixed, regardless of the changes between the years. In other words, the amount of renewable water that the earth's surface receives now and annually is equal to the water that it may have received thousands of years ago since the dawn of human civilization. This is while the spatial and temporal distribution of the amount of renewable water is completely variable and is not commensurate with the distribution of population and water needs of human societies. Obviously, the most accurate rainfall measurements are provided using traditional methods such as climatological stations, ground synoptics and rain gauges, but the accuracy of using this method in a dependent catchment Due to the spatial distribution of the stations and the sensitivity of its trustees in the maintenance of the stations, the realization of this important issue requires a lot of money and time. Accordingly, and since remote sensing is able to provide continuous, homogeneous and near-real-time information on the location and time in a particular area, even in inaccessible areas. There are several factors involved in evapotranspiration, the degree of effect of each of which depends on other factors, which makes it difficult to accurately estimate evapotranspiration. Some of these factors are air temperature, relative humidity, atmospheric pressure, amount of water-soluble substances, altitude and longitude. Thus it is impossible to use traditional methods and to directly calculate evapotranspiration without any degree of accuracy over a wide range. When the amount of rainfall in a catchment is more than the soil infiltration capacity, part of the rainwater flows as runoff on the surface of the catchment. The amount of runoff in a catchment depends on several factors including soil characteristics, catchment concentration time, geological conditions, climate and climate, slope and direction of slope, land use and drainage density. Various devices such as lysimeter and TDR device can be used to estimate the penetration rate directly; However, it is obvious that using these devices to estimate the infiltration rate in a large catchment area is very time consuming due to changes in the soil type of the catchment. Therefore, this component is calculated through methods, equations and relationships resulting from some hypotheses in hydrology. It is obvious that the accuracy of ground stations is much higher than satellite data, but in large basins due to the high cost of construction and maintenance of these stations and also the time consuming use of it. This study was conducted in the location of Tajan catchment area, which seems to be the ground stations located in it, both in terms of number and in terms of health due to the prosperity of agriculture and the location of Sari city with the majority of farmers in the region compared to other basins.}, Keywords = {emote Sensing, Water Balance, Tajan Watershed, TRMM Satellite, MODIS Sensor, SCS-CN Runoff Equation}, volume = {11}, Number = {1}, pages = {161-175}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1027-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1027-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Jafari, J. and Mesgari, M. S.}, title = {Generalization of Multi-linear Feature Based on Ordinary Least Squares Regression}, abstract ={For the preparation of small-scale maps from large-scale maps, generalization of vector features is important. This method increases the quality of maps published, enables the analysis of data at various levels of detail, and reduces the volume needed to store them. The methods of linear and polygonal features generalization are performed with the aim of preserving their geometry and area while reducing their details. Various models have been used and evaluated by researchers in this field; However, most of them summarize the features with the aim of selecting a few points from them and deleting other points. Even so, the deleted points may contain valuable information for this complication and their removal will lead to a defect in its geometry and area. In this study, the generalization of multi-linear features was performed using minimizing the vertical distance from the main line. In order to study the proposed model, after its implementation on different shapes, the multi-lines of Lake Urmia and its islands were generalized and the results of the proposed model were compared with the common Douglas-Poker and Viswalingam methods. The results were then evaluated using the indices of area differences, the similarity of the mean curvature, the similarity of the amount of angle changes and the modified average Hausdorff distance. The results showed an average superiority of 99.91, 66.29 and 60.99% compared to conventional simplification approaches with proposed model (in the first three indicators). The proposed model has a advantage over the Douglas-Poker and Viswalingam methods 0.16 and 0.2 percent based on the area difference index, 7 and 5 percent based on the average curvature index, and 6 and 2 percent based on the  sharp change index. but In the the modified average Hausdorff distance index,  it was about 2 meters worse than the aforementioned methods, due to the lack of reliance on the initial points of the complication.  }, Keywords = {Generalization, Least Squares, Douglas-Poker, Viswalingam, Regression}, volume = {11}, Number = {1}, pages = {177-189}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1008-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1008-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {EbrahimianGhajari, Y. and Valizadeh, M. and Hosseini, H.}, title = {Indoor Routing by building a three-dimensional route network based on integration of BIM and GIS}, abstract ={With the development of complex urban infrastructure such as high-rise buildings, the importance of geospatial information in the interior of buildings becomes more apparent. Routing is one of the most basic processes related to geospatial information that existence of a precise and complete network of the desired environment is necessary for its efficiency. In most cases, a data set from the target network is required before performing any analysis on a route network; Having such data outside the building, such as Google Map and OSM data, as well as accessing it is much easier than inside the building. Access to such information requires geometric information and in some cases accurate semantic information of the building. In this regard, building information modeling technology (BIM), by providing useful information about buildings can help to build a route network in its interior. On the other hand, in order to complete the routing process, in addition to the route network, efficient tools for analysis on the route network are needed, which are not available in BIM. In this regard, Spatial Information Systems (GIS) have an ability to perform the required analysis in the routing process. Therefore, it seems that by combining BIM and GIS, the ground can be provided for better management of the routing process inside the building. In this research, the combination of BIM and GIS has been done with the aim of building an indoor route network using FME Reader software. In this research, the campus of the Faculty of Engineering, University of Tehran has been considered as the study building. In order to prepare a three-dimensional model of these floors, Revit Architecture 2017 software has been used. First, the two-dimensional map related to the plan of these classes is entered into the software environment and then the three-dimensional process is implemented in accordance with the necessary standards. After extracting the required data in the BIM model, this data has become the standard format in GIS. In order to eliminate the errors resulting from the conversion of data format from BIM to GIS, such as overshoot and undershoot errors, concepts and topological analysis in GIS have been used. Finally, the two proposed methods Mesh and TIN have been used to create the route network. The Mesh method uses regular grading of the region to create the route network, while the TIN method uses the Delaunay triangulation concepts to construct the route network. In the Mesh method, the number of points involved in routing is very high. High number of points increases the accuracy of routing, but processing these points in order to find the optimal path requires high hardware. The TIN method uses triangulation to network the environment, and therefore, due to the low number of points involved in the path network, information processing in order to find the best path requires less hardware power. Also in this study, the stairs were connected to each other as a straight line, which reduces the accuracy of routing. Routing can be used in technologies such as in-house positioning. The results of the research show the efficiency of the information in BIM models as well as the proposed methods in constructing the route network in the routing process.}, Keywords = {GIS, BIM, Three-dimensional Grid Route, Indoor Routing}, volume = {11}, Number = {1}, pages = {191-204}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1013-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1013-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Jafari, J. and Jiryaee, A. and Mesgari, M. S.}, title = {Modeling the Spread of Infectious Diseases Malaria}, abstract ={Malaria is a parasitic disease transmitted by mosquitoes that kills 1.5-2.7 million people per year.  Malaria remains a major concern for the World Health Organization despite extensive research. Computer simulation models have been used to research disease carrier management strategies in recent years. The development of a simulation model based on the Agent modeling of the malaria is being considered in this study. Given that the spread of this disease depends on various environmental, geographical and social factors. Therefore, this study focuses on evaluating the impact of effective environmental and geographical factors, on the spread of the disease in Bandzarak rural district of Hormozgan province and also examines the impact of control measures and policies to prevent the spread of the disease. Humans and female Anopheles mosquitoes were used as factors in this study to model the spread of malaria, and the land cover map of the study area was used as a simulation environment. Temperature, humidity, distance from stagnant water, distance from vegetation, and human population density were chosen as effective factors in the spread of malaria in this research. In order to test and evaluate the model, four experimental scenarios were designed, during which the effect of different factors in the model was investigated. The results showed the sensitivity of the model to these factors and the relative superiority of 20% of the recovery parameter in controlling this disease. It was also shown that simple scenarios can show and evaluate the effectiveness of control measures in reducing and controlling the disease. Control policies are meant to avoid disease transmission by drying stagnant water, using protective nets, killing mosquitoes at the start of the warm season, and deteriorating health conditions, with the result that using protective nets is the most effective way to prevent disease transmission.}, Keywords = {Malaria, Agent Based Simulation, Environmental Factors, GIS}, volume = {11}, Number = {1}, pages = {205-219}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1016-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1016-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Bahadorizadeh, H. and Malek, M. R. and Zoraghi, M.}, title = {Design and Implementation Volunteered Geographic Information System for Gathering Agricultural Products Information}, abstract ={The importance of information has led researchers to look for the least cost and time ways to acquiring information. One of the newest mechanisms of acquiring and compiling geographic data is Volunteered Geographic Information (VGI). This way saves time and money and can provide regularly updated geographic information that is difficult to obtain through remote sensing but can easily be collected by citizens on the ground. VGI refers to geographic information created by citizen volunteers. VGI has proliferated in recent years, mainly due to the technological advancements enabling the public to contribute geospatial data. One of the fields that we can use VGI is agriculture. In this way, every farmer, as a citizen volunteers, records the information of his agricultural lands. Now, field visits and satellite image processing are the two main ways to collect this information. Both of these methods are costly and time-consuming.  In this article, we propose a VGI system to gathering the type of cultivation information and the spatial boundaries of agricultural lands. For evaluation, the proposed approach was implemented for a study area and thatchr('39')s results were compared with the remote sensing method. The results show that the accuracy of the information gathered with the proposed method, which obtained 91% by the confusion matrix. This accuracy is approximately equal to the accuracy obtained from the remote sensing method; While the time and cost of this method are much less than the remote sensing method. Also, the precision of the collected data according to the confusion matrix for the four crops of corn, rice, wheat, and barley in this study were 0.91, 0.89, 0.86, and 0.91, respectively, the results were reliable. This shows the reliability of the results obtained from the VGI for different products.}, Keywords = {Geospatial Information System, Volunteered Geographic Information, Agriculture, Satellite Images}, volume = {11}, Number = {1}, pages = {221-237}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1024-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1024-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Shakhesi, M. and Alesheikh, A. A. and Habibi, Roy}, title = {Gazetteer Enrichment Using Real Estate Advertisements}, abstract ={Introduction Gazetteers are geospatial dictionaries of geographic names containing triples of place names, geographic footprints, and feature types for named geographic places. As an important element in Geospatial Information Retrieval (GIR), these precious resources should be enriched according to new applications. . Identification and adding new place names to the gazetteer, and keeping it up to date are important issues in the gazetteer enrichment. The main challenge in this era is that in most gazetteers only a top-down approach is considered. Consequently, most local place names are ignored in such gazetteers. In addition, updating gazetteers is a time-consuming and expensive process. Since the emergence of Web 2.0, using volunteered Geographic Information (VGI) and social media in harvesting place names have been attracted the attention of many researchers due to containing local place names and recently created ones. In a similar condition, online property ads published by people contain such place names. This article presents a data-driven method for identifying urban place names including neighborhoods and main streets using online real estate advertisements. Materials and Methods The online real estate ads of four metropolises including Tehran, Mashhad, Isfahan, and Shiraz mined from the Divar website. After n-gram extraction and applying required pre-processes, the n-grams got labeled. To remove outlier points from an n-gram set and consider the scenario that several places can have the same name through a city, the point set of the n-gram get clustered. Based on a set of spatial statistics, the random forest models on housing data of each city trained and then tested on the ads data of other cities. Discussion and Results The results show that either in detecting the main street or neighborhood, the model trained on ads data from one city has a successful prediction on the other ones. For example, the models trained based on the data of Tehran and tested on the data of Mashhad achieved 61% and 74% respectively in identifying street and neighbourhood. However, for some reasons such as imbalancement of datasets, data labeling challenges, and in some cases, identifying non-spatial n-grams due to clustering, precision has been decreased. Also, because of differences in urban patterns and place naming patterns between the cities, the recall has been slightly decreased. Conclusion A place can be referenced in two different ways: 1- By calling its name and 2- By coordinate data. Gazetteers are considered a bridge between that two types of georeferencing. According to the importance of these resources in geospatial applications, the enrichment of them is a necessity. For containing local place names, online property listings can be considered as a valuable resource for harvesting toponyms and enriching gazetteers. Regarding to that most users in publishing online property, ads consider a neighborhood or main street name which is well-known for the readers, these place names usually are written without any clue for identifying a location in a text processing manner. The behavior with respect to a set of spatial statistics can be considered as a spatial signature to recognize an n-gram as a neighborhood or street place name.  }, Keywords = {Gazetteer Enrichment, Geospatial Information Retrieval, Real Estate Advertisements, Random Forests}, volume = {11}, Number = {2}, pages = {1-14}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1004-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1004-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {PanahiAzad, A. and Arabi, M.}, title = {Development of a web-based system for scoring residential properties based on the public transportation accessibility index}, abstract ={One of the central issues in choosing housing (buying or renting) is having a high degree of access to various types of urban transportation networks. Because the low-income groups of the society use public transportation more for traveling in and around the city, and this issue is so important in choosing a place of residence that it is one of the main local quality indicators and an effective factor to save the household budget, especially for the low-income sections of society. Therefore, the development of a score-based system and scoring of real-estate and housing based on access to the public transportation system is valuable. Analysis of previous research shows that the importance and impact of transportation systems in property valuation, from the distant past to the present and, as in the past, has a decisive effect on property placement, especially in the selection of residential areas; In this regard (based on the summary of the researches - Table 1) can be categorized and proposed in several scenarios. Scenario 1. Survey and ranking using socio-historical intelligence: In the past, human memory and quotes from others about the tranquility of neighborhoods were questioned, and this method is still common among many traditional communities. This type of ranking is qualitative and the indexing is very low or the parameters have not been quantified yet. So that in no visible place has it been given a privilege except in the public mind. In this method, it is evaluated only based on the needs of the society and the demand of the people and the type of their behavior with the environment. Here, the desirability of the place is considered according to the social desirability and without the desirability of other parameters. And it is a kind of use of public trust in society. Scenario 2. Review and rank the parameters based on the value from the customer's point of view: Determine the value from the point of view of a particular person or group without considering the acceptance of the community. Customers who value the proximity to their place of business value the property more without the need for transportation services. Scenario 3: Review and ranking with the characteristics of the type of building and local coordinates (local): In such a case, the price evaluation is based on the characteristics of the neighborhood such as connection and density of streets, parking, garbage disposal, etc. Also, determining the price of a house is related to the type of building (apartment or villa), size and age of the building, number of rooms, water system, gas, swimming pool, heating services, cooling, etc. Scenario 4: Review and ranking with purely economic parameters: Based on the economic issues raised in the study community. This means that the value of the property is considered based on proximity to commercial real-estate or suitable job opportunities. On the other hand, the income and financial capacity of each person determine these types of values. Scenario 5: Review and ranking with comfort-oriented parameters: Here the value of the property is a function of other determining parameters, since the relaxation and comfort coefficient of each person is different, the criteria of comfort and relaxation, change over time and the value of the property will also be subject to these changes. For example, for one person with a student, proximity to school and educational services is a priority, and for another, distance from school and the resulting congestion is a parameter of comfort. Scenario 6: Survey and ranking with the parameters of social welfare and quality of life (social life average): One of the most important criteria for valuing property are human welfare factors, including proximity to the urban transportation system (Metro, Buses and BRT), education, health care, employment and leisure. Meanwhile, in densely populated cities, the urban transit system is an important factor in improving the quality of human life and easier access to amenities. Even the distance from the property to the transportation system will significantly determine the value of the property. In this study, while reviewing examples of previous studies, their methods and results were specifically categorized in the above six scenarios, then by determining and classifying the indicators of each scenario, a new scenario called scoring and ranking with temporal and spatial variables (spatial effect of parameters and combination of previous scenarios) were presented. Also, the useful access radius of transportation stations was identified and verified based on the study of previous researches, consultation with specialized (academic) experts and local experts; then, using the fuzzy method, a mechanism for determining the characteristics, ranking and scoring was designed to create a hierarchical-scoring system with the algorithm of combining multidimensional real estate scores. This ranking provides a quick and easy comparison of residential properties in multiple locations in the city in a reliable environment. The proposed system has three layers: data, logic and representation; In the coordinate data layer and descriptive and analytical information, the properties in the study area and metro, bus and BRT transport stations are recorded. In the logic layer, the fuzzy web service is used for fuzzy inference and the web server is used for the display layer to interact with the data layer. The results of the evaluation of the designed fuzzy scoring system, in comparison with the field evaluations and the results of other systems, confirm the efficiency of this system. Scenario 7: Review and ranking with temporal and spatial variables (spatial effect of parameters and combination of previous scenarios): The effect of parameters valuing property, based on the need that exists in a particular place or time. For example, for the needs of a customer, which is valuable only in a limited time and place, the use of public transport system, as a result of the spatial impact of such factors, depending on the time and place of customer needs, definition and specialized scoring (Non-public) is presented. Sometimes a particular job situation requires a client to prefer a particular location only in certain seasons. Also, maybe the buyer or lessor of the property, each of them considers different and various parameters as value. As a result, for a person who is going to live in a place for a long time or a person who uses that place temporarily and for a short time, a series of short-term cross-sectional and long-term stable parameters will affect the value of the property. As a result, different results at different times and places provide maps for decision making. Given the above, it is necessary to emphasize that the scenarios have integration and correlation and all of them have weaknesses, the structure and array of measurement, quantification and indexing systems, due to the lack of the same regional, urban and meta-indicators, their performance on different scales is somewhat problematic. On the other hand, the indexing system is largely conventional and varies from place to place and in each geographical area. Therefore, the system of these models cannot be the same everywhere unless we can change the indicators. Table 1 summarizes the articles of various researchers in this field with emphasis on access to the public transportation system. Understanding the significant impact of the transportation system on property prices and its undeniable effects on the well-being and quality of life of people, designing a web-based system with dynamic and changeable information is important for public access. In our country, there is no comprehensive system that contains property price information with emphasis on access to the transportation system, so the development of a web-based system for rating residential real-estate based on transportation, as the goal of this study, is important. Especially to design a system that maintains its dynamism and capabilities due to the dynamism of urban development plans and plans, diversity and multiplicity of laws, the existence and intervention of various decision makers in urban affairs and the sharp fluctuation of housing and property prices that do not make gross errors during the oscillation periods; Also, the designed system can have its unique features, the possibility of registering the property by users and the possibility of displaying it on the map and viewing transportation scores.}, Keywords = {Web-based System, Real Estate, Transportation Systems, Fuzzy Inference}, volume = {11}, Number = {2}, pages = {15-26}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-831-en.html}, eprint = {http://jgst.issgeac.ir/article-1-831-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {KhaniPordanjani, H. and Torahi, A. A. and RiahiBakhtiari, H. R. and Sadeghian, S. and DivistiMoghandari, M.}, title = {Extraction of Hidden Forest Roads Using LiDAR Data (Study Area: Shast Kalate Forests of Gorgan)}, abstract ={Forest roads are essential for forest management, forest harvesting, wood transportation, recreation, education, research, and forest protection. To meet these needs, forest road networks have been constructed in the northern forests of Iran. Forest road mapping especially over large and mountainous areas is time-consuming and expensive. Today, remote sensing data can be considered as an important tool for forest roads extraction. Therefore, in this research, LiDAR data and UltraCam images were applied in order to extract hidden forest roads. At the first step, noise points in the point cloud data were removed. Then, according to the Central Limit Theorem (CLT), the third statistical moment (amount of skewness) of the data was calculated and the non-ground points were eliminated. At this next step, a number of non-ground points were identified as ground points. In order to eliminate these errors, slope-based algorithm with a radius of 10 meters and a slope of 22 degrees was applied on the points obtained from the first step, these points were eventually removed and the ground points were extracted. Then, extracted ground points were converted to grid. Then the grid was converted to polygon based on the pixel density, by using the DTM as well as UltraCam aerial images, polyglots that were not related to the road were removed. Until this stage, the output was the roads that were not hidden by the forest canopy. Therefore, the hidden parts of the roads were extracted by applying slope-based algorithm with the radius of 10 meters and 65 degrees slope on the whole LiDAR points and interpolating the results by spline interpolation method. By connecting and modifying the polygons, 3m wide dirt roads and 2m wide skidding roads were extracted. The results are evaluated by comparing to manually acquired road data. The quality measures completeness, correctness and quality were 82%, 86% and 72%, respectively.}, Keywords = {Forest Roads, LiDAR Data, Slope-base Algorithm, DTM, UltraCam Aaerial Image}, volume = {11}, Number = {2}, pages = {31-43}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-915-en.html}, eprint = {http://jgst.issgeac.ir/article-1-915-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {EbrahimianGhajari, Y.}, title = {Evaluation of Physical Resilience of Urban Areas from the Perspective of Passive Defense Using Geospatial Analysis and PROMETHEE II Technique (Case Study: Babol City)}, abstract ={So far, a lot of research has been done to assess the resilience of the city against a variety of natural threats such as earthquakes, but research on the assessment of resilience of urban areas against man-made threats, especially war threats, is far less that in this study Is. In fact, what poses a serious threat to military strikes is the unpreparedness to deal with them, and the best way to deal with these threats is to build and maintain preparedness against them. One of the main ways to prepare for crises is to be aware of the city's resilience in the event of a crisis, in which case, by adopting strategies, preparedness for such crises can be greatly increased. In the present study, the physical resilience of the seven areas of Babol has been evaluated from the perspective of passive defense using geospatial analysis and PROMETHEE II technique. In this study, first, using the opinion of experts in the fields of urban planning and passive defense, the basic threat of the city of Babol (air attack) was selected. Then, based on this and by studying previous research and obtaining the opinions of experts in the field of urban planning, passive defense and structure through interviews and questionnaires, sixteen criteria affecting the physical resilience of Babol in three categories of distance from special uses, access to main services and features the urban physical tissue was extracted and weighed. These criteria are: Distance from military bases, Distance from key stairs, Distance from refueling centers, Distance from the utility network, Distance from industrial centers, Network access the main way, Distance from fire stations, Access to medical centers, Outdoor access, Degree of confinement, Build density, population density, Number of floors of buildings, Skeleton type of buildings, Granulation of parts, Age of buildings. Then, the raster criterion maps were generated and the average values ​​of each criterion for each of the seven districts of the city of Babel (as alternatives for multi-criteria decision making) were calculated and the decision matrix was created. By generation the decision matrix and using the PROMETHEE II method, the 7 districts of Babol city were ranked based on the degree of resilience and a physical resilience assessment map of Babol city was generated. The results showed that different areas of the city of Babol do not have the same resilience, so that the central areas of the city and to some extent the southern areas of the city have lower resilience than the northern, eastern and western areas. In general, with increasing distance from the city center, resilience increases, which is less felt in the southern direction. Although the city of Babol has moderate to high resilience in general, but by resilience analysis, the most important reasons for the low resilience of the central areas of the city, namely regions 4 and 5, can be obtained. Analyzing the research results by passive defense experts, it was found that the most important reasons for low resilience in the central urban areas of Babol (areas 4 and 5) are high construction density, high degree of confinement, important military centers and numerous refueling centers in these areas.}, Keywords = {Urban Resilience, Passive Defense, PROMETHEE II, Geospatial Multi-Criteria Decision Making, Geospatial Analysis, Babol}, volume = {11}, Number = {2}, pages = {45-60}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1032-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1032-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Maleki, M.}, title = {Estimation of Surface Roughness Parameters Using Fractal and Close Range Photogrammetric Methods}, abstract ={Nowadays, surface roughness measurement has been considered in many civil and industrial applications. One of the important indicators in measuring the roughness of the surface, is determining the fractal dimension. To determine the fractal dimension, the profile meters are usually used by contact method, which in the case of surfaces with low roughness may cause distortion of the surface texture and as a result, the roughness is measured with low accuracy. In this study, a fast and reliable method based on close range photogrammetry, which is a non-contact method, for measuring fractal dimension and roughness is presented. The case study in this research is the sand surface.. Digital elevation model was created by taking several overlapped photographs. For accurate measurements on the surface 6 control points were created, then scaling and definition of the coordinate system was performed. The accuracy in x-y plane is 1.33 mm and the height accuracy is 0.32 mm. In order to measure the surface roughness parameters including correlation length and root mean square height (rms-height), several profiles were extracted from the three-dimensional surface. The slope of the surface spectrum was calculated using Welch method for each of these profiles then the fractal dimensions were calculated. Finally, using the fractal dimension, the correlation length and rms-height were calculated for each of the profiles. In this study, surface roughness was evaluated using four indices of fractal dimension, correlation length, rms-height and ZS parameter (ratio of rms-height to correlation length). ‌ The results showed that for all profiles, ZS with 81% correlation coefficient with fractal dimension, is a sufficient indicator for calculating the surface roughness, because of the the independence of this indicator from the length of the profile. This study showed that the close-range photogrammetry is a very suitable and reliable method for measuring roughness parameters. One of the important advantages of this method, unlike other methods such as needle or laser profile meters, is having multiple profiles in any desired direction and preserve of texture (especially on very low roughness surfaces) due to direct contact of the profile meter with the surface.}, Keywords = {Close Range Photogrammetry, Three Dimensional Model, Surface Roughness, Correlation Length, RMS, Height}, volume = {11}, Number = {2}, pages = {61-73}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1019-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1019-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Emami, H. and Asadzadeh, S.}, title = {A Synthesis Approach for Optimal Determination of Solar and Geothermal Locations Considering Economic, Environmental, and Demographic Criteria Using RS and GIS Data}, abstract ={Energy is an unavoidable requirement for humans. However, due to population expansion, the energy source is expected to become limited in the next years. As a result, consumers favor renewable, clean, and cost-effective energy sources. Unfortunately, there is no one source of energy that can fulfill these needs. Within the framework of a long-term and strategic process, determining the right energy policy problem with interactive criteria and alternatives can be seen as a multiple criteria decision making (MCDM) problem. One of the features of Iran's energy system is its reliance on imports. Different sets of decision-makers are engaged in the process of picking one of several Renewable Energy investment projects. Because of the increasingly complicated social, economic, technical, and environmental elements at play, decision-making must take multiple competing agendas into account. Traditional single-criterion decision-making is no longer capable of dealing with these issues. The VIKOR technique, also known as the Compromise Ranking method, introduces the Multi-criteria ranking index based on a specific measure of "closeness" to the "ideal" answer. The approach is used with the Analytical Hierarchy Process method to weight the relevance of the various criteria, allowing decision-makers to give these values depending on their preferences. The integration of renewable energy supply systems into energy supply networks is a critical lever for addressing the issues of sustainable development and climate protection. The synthesis issue, on the other hand, is an intrinsically complex process for which three hierarchically dependent layers must be considered. The configuration level is where equipment selection is made, the sizing level is where equipment capacity is calculated, and the operational level is where real load dispatch is specified. While these levels must be considered for any energy system, dealing with the complexity resulting from the temporal and spatial interdependencies associated with renewable resources, which usually necessitates the installation of storage systems, is a key challenge in the synthesis of renewable energy systems. Furthermore, the diversity of available technologies and conceivable combinations adds to the complexity. Furthermore, because the adoption of renewables is still often motivated by environmental concerns, both the economic and ecological consequences must be addressed. Thus, complicated linkages and trade-offs between technological, economic, and ecological implications must be weighed in order to identify the optimum solution for a specific synthesis challenge. This study presents a synthesis approach for determining optimal solar and geothermal sites of the output layers of the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, as well as a multi-criteria analysis of various environmental, socioeconomic, remote sensing data, and spatial information system. Identifying both prospective solar and geothermal locations together or adjacent to each other can not only provide a complementary output, but by combining these two energies, it addresses the deficiencies in each of their separate performance. The designation of such regions necessitates a thorough understanding of their efficacy elements and criteria. To that aim, fifteen distinct data layers, as well as Landsat 8 satellite imaging, were used in northern Iran over two periods of cold season for ground energy and warm season for solar energy, digital elevation model and its derivatives. First, multiple types of study data are quantified, weighted, and then merged. Following that, for economic evaluation of the results, the two main factors, demographic-industrial centers and development of these centers, are experimenters and analyzed, and become a more suitable construction of power plants were classified into five classes: poor, medium, appropriate, and very appropriate. Separate analyses for solar and geothermal energy show that about 51% and 30% of solar and geothermal energy, respectively, are found in acceptable and extremely suitable regions. Furthermore, when economic considerations and population-industrial hubs are taken into account, the combined findings of these two energies show that almost 59% of the territories represent areas prone to solar energy and geothermal. These locations in the province's south, southeast, and central regions, as well as the province's north and northwest, have high potential for geothermal and solar energy.}, Keywords = {Solar and Geothermal Energy, Renewable Energy, Remote Sensing, GIS}, volume = {11}, Number = {2}, pages = {75-98}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-996-en.html}, eprint = {http://jgst.issgeac.ir/article-1-996-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Karimi, A. and EbrahimianGhajari, Y.}, title = {Spatio-temporal Analysis of Land Surface Temperature Using a Genetic-Based Selection Procedure (Case Study: Tehran, Iran)}, abstract ={Introduction Due to the rising global temperature, especially in cities, which is often due to urban and population growth, followed by life, financial and environmental risks, identifying the factors affecting the Land Surface Temperature (LST) in urban areas is of great importance. Rising temperatures in urban areas have created a phenomenon called Urban Heat Islands (UHI), which is a very dangerous phenomenon for humans and the environment. Therefore, by identifying these factors, we can prevent this phenomenon as much as possible by using public education to the people, enacting effective management laws and policies, and more monitoring to deal with the stimuli of rising the LST. The main reason for the increase in LST in urban areas is due to urban growth in two-dimensional and three-dimensional direction, and this phenomenon is intensified by population growth and land use changes. Numerous other factors, such as the heating of buildings, air pollution, and the use of unsuitable materials such as asphalt, which absorbs sunlight, cause rising the LST in the streets and alleys. The purpose of this study is to identify the spatial factors affecting the LST in urban areas during a certain time and predict it based on the effective factors determined by the proposed algorithm. In this study, a selection method for identifying the effective factors in predicting Land Surface Temperature in urban areas through the combination of Genetic Algorithm and Geographically and Temporally Weighted Regression was presented. To evaluate the proposed method, nine factors including land use, distance to roads, population density, construction density, air pollution, aspect, slope, building height and elevation were used in this study as the spatiotemporal factors to predict Land Surface Temperature in a desired time in Tehran city, Iran. Materials & Methods In this study, an attempt was made to identify and investigate some factors affecting LST in urban areas. Attempts have also been made to consider both natural and human factors. These factors include elevation, aspect, slope, land use, distance to roads, population density, construction density, air pollution and building height. In order to find the most effective factors on LST, a combination of genetic selection method and Geographically and Temporally Weighted Regression (GTWR) was used in which LST is estimated through the spatial data during a certain time. . The optimal factors are selected to predict LST by combining the genetic algorithm with the GTWR function. By the development of the time dimension of the GWR model, the GTWR model was introduced, which includes bandwidth and the spatio-temporal kernel function. In Geographically and Temporally Weighted Regression, it is assumed that data have autocorrelation and temporal instability in addition to spatial autocorrelation and instability. That is, regression coefficients, in addition to changing from point to point, also vary for one point at various times. Results & Discussion In order to implement the proposed algorithm, 1500 points were randomly generated in the study area. All input data in ArcGIS software was converted to raster format so that the values of this data could be assigned to these random points. After preparing all data based on the generated points, these data were entered into the proposed algorithm and finally 5 factors out of the 9 factors were identified as effective factors. The value of R2 obtained by GTWR method with 5 identified effective factors was 0.9882 and using all available factors 0.9621 which shows the improvement of the results and high compatibility of this model with selected factors. Also, the RMSE value was 0.5097 and the normalized RMSE value was 0.1908, which indicates the high accuracy of this model. Conclusion Using the proposed algorithm, 5 factors including elevation, distance to roads, land use, construction density and air pollution were selected as effective factors for predicting LST. The results of this study indicate that the prediction results using the selected factors are more accurate than the same results using all factors. Accordingly, the experimental results of this study show the appropriate performance of the proposed method in predicting LST. Finally, due to data availability problem in this study, it is suggested to increase the number of factors in study area, especially human factors in future works. Traffic in cities, urban geometry, types of land uses are among the important human factors that can be effective in LST.}, Keywords = {Land Surface Temperature, LST, Urban Heat Islands, Spatio-temporal Analysis, GTWR, Genetic Algorithm}, volume = {11}, Number = {2}, pages = {99-113}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1040-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1040-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Pourmina, A. H. and AzmoudehArdalan, A. R.}, title = {Bathymetry Mapping of the Jask Port Using Sentinel-1 SAR Images}, abstract ={Knowledge of underwater features and ocean bathymetry plays a significant role in coastal engineering and management, resource exploitation, navigation, research on tide and biodiversity, planning for seawalls and wharves, offshore fisheries, and aquaculture. Shallow water depth surveying is essential for the protection of vessels during navigation. Conventionally, depth charts are obtained by sonar measurements carried out from dedicated vessels, which are expensive and time-consuming, and problematic in shallow water areas. Remote sensing techniques may offer an alternative method that can be used to reduce the cost and labor needed for underwater topographic surveying and mapping. Therefore, remote sensing, due to its high ability to collect information in a short time and a wide geographical area, will be a very suitable solution for many studies and engineering projects in coastal areas. This issue is critical in Iran, which has a long coastline. In this study, an attempt was made to prepare a depth survey map of the coastal areas of the Oman Sea coast of Jask port region by implementing the swell wave spectral pattern analysis algorithm on Sentinel-1 satellite images. Synthetic Aperture Radar (SAR) is an active remote sensor that is fairly weather insensitive and has global coverage. It provides two-dimensional (2D) information on the ocean surface. Under favorable conditions, it can detect topographic seafloor features in shallow water areas. This paper presents a practical method for shallow water depth estimation based on swell patterns in SAR images with two different data for wave period. This method is based on the refraction and wavelength changes of swell waves in coastal waters and can measure depth in medium-depth waters. The Linear Wave Theory in waters with medium depth establishes a relationship between depth, wavelength, and period of swell waves. Two-dimensional Fourier analysis was used to calculate the wavelength of swell waves, and the accuracy of calculating the wave peak was increased by applying a frequency filter. One scene of Sentinel-1 SAR image over the Jask coastal region is chosen for investigation. The estimated water depths from the SAR image using the ERA-5 wave period are compared with the Bathymetric map of Jask. The average absolute error is within 0.992 m, with root mean square error within 1.361m with the average relative error less than 6.05%. The estimated water depths from the SAR image using the estimated wave period from the deepwater image are compared with the Bathymetric map of Jask. The average absolute error is within 0.811 m, with root mean square error within 1.125 m with the average relative error less than 5.051%. This indicates that the estimated wave period from the deepwater image is better for water depth estimation than using the ERA-5 wave period. Also, Sentinel-1 SAR can detect wave shoaling and refraction in depths between 10 m and 25 m because of its high resolution. The method can be used for water depth with a 10 m detection with reasonable accuracy within the test area. Using Spectral Analysis in Deep Water Range make 17% improvement in depth derivation Accuracy Compared to the Global ERA-5 Wave Model in the Range of 0 to 25 Meters, and 30% Improvement in the Range of 20 to 25 Meters.}, Keywords = {Bathymetry, Radar Images, Spectrum Analysis, Swell Waves, Jask Port}, volume = {11}, Number = {2}, pages = {115-127}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-959-en.html}, eprint = {http://jgst.issgeac.ir/article-1-959-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Mirzahossein, H. and Zamani, A. H. and Hajiseyedrazi, N.}, title = {Investigate the Application of machine learning and Agent-Base Models in Land-use Planning}, abstract ={Population growth and increased migration from rural to urban areas have led to widespread climate change that has significantly impacted land use. Urban sprawl is a phenomenon that happened these years, especially in developing countries. Therefore, planners have always been looking for methods and models that simulate the expansion of urban and climatic land use well to prevent the unbalanced growth of cities, climates, and undesirable development problems. These models guide them to manage the plans in the desired direction. Advances in artificial intelligence in recent years, along with widespread access to online data, the emergence of new methods of big data analysis, and the development of advanced technologies, have led to the emergence of new technologies and methods such as machine learning techniques and agent-based modeling. Investigating Iran's policies on land use issues and developing new solutions with considering a comprehensive review of data-driven methods are needed to analyze the problems and solve the problems resulting from these changes. In addition to data-driven approaches, the specific benefits of factor-based models include their ability to model individual decision-making institutions and their interactions, the combination of social processes and non-monetary influences on decision-making, and the dynamic linkage of social and environmental processes. Therefore, classification, forecasting, modeling, and simulation to estimate the future situation with the help of data from these changes in different periods can be the basis for making the right decisions in the current situation. In this regard, experts in this field have always considered the use of new strategies for land modeling and land use planning. Although extensive studies have been conducted in the field of machine learning (ML) methods as a new approach to classification, prediction, simulation, and modeling in various fields of science; However, these studies have less reviewed the proposed and applied methods of the agent-base modeling and machine learning in the analysis and modeling of land-use change studies. To this end, this article provides the opportunity for a systematic review of the application of machine learning algorithms and agent-based modeling, which has been recorded in the most critical research and experimental evidence of the United States, Europe, and various parts of Asia, especially East Asia and also Iran. Therefore, the different algorithms and methods implemented in each study are reviewed, and the results of data analysis are presented accordingly, which can be the basis for further research to use widely used, accurate and dynamic models. This study shows that different land use issues such as classification, forecasting, and simulation require algorithms with appropriate structure. Results show that no method and algorithm can be considered absolutely superior compared to other methods and algorithms. Thus, the most widely used methods for classifying, predicting, and simulating land-use change are categorized in this paper. In general, it was also found that support vector machine (SVM) and Convolution Neural Networks (CNN) as widely used methods, with the best results, provide valuable solutions for land use classification, forecasting, and simulation.}, Keywords = {Land-use Planning, Machin Learning, Land Use Change, Agent-based Model}, volume = {11}, Number = {2}, pages = {129-152}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1017-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1017-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Bahrami, N. and Argany, M. and NeysaniSamani, N. and VafaeiNejad, A. R.}, title = {Designing a Context-aware Recommender System in the Optimization of the Relief and Rescue by Ant Colony Optimization Algorithm and Geospatial Information System}, abstract ={One of the significant dangers to human life is crises and natural disasters worldwide every year. If such incidents are unpredictable, their risks and casualties will be much higher. Among disasters are floods, hurricanes, volcanoes, earthquakes, tsunamis. An earthquake is an event that is more prevalent than other disasters and is almost unpredictable. Respond structure to crises and disasters is called crisis management which deals with all issues before, during, and after crises and disasters and leads to activities in the field of planning, preparedness, prevention, response, and reconstruction. One of the most basic and essential things that can reduce the casualties of various events is disaster relief and rescue, which respond to the crisis management structure. Also, the contextual information of the environment, rescuers, and activities created a context-aware recommending system that can facilitate the process of interaction with the environment. This study has checked the types of contexts, their relationship, and the structure of earthquake rescue in Iran, where there is a significant crisis due to geographical location and seismicity. The whole problem space consists of three parts to provide a meaningful definition of the concept of "context" in the deployment of relief and rescue teams. The rescuer is the main context. Relief and rescue team as the object's environment, which includes team information of rescuers, consists of team members' position, distance, physical condition, and activity compared to other rescuers in the group. The physical environment is a collection of injured people, buildings, and relief and rescue teams in a specific area. Contexts of rescuers and their relationships, teams, and the hypothetical earthquake were studied by studying related articles and books and interviewing experts in the field of research. The study of context-aware and optimization methods used for the actual structure of rescue teams are the innovations of this study. Contexts during relief and rescue include location, time, the extent of human and building injuries, rescuers' interactions with and with the environment, and activities, rescuers' specialties, and priorities. Interaction is necessary for optimal management of relief and rescue. To create a relationship between the various contexts and optimize the relief and rescue process by defining the mathematical function and using sensed information from contexts into the proposed optimized algorithm. Finally, the solution has been designed and implemented with an ant colony algorithm and geospatial information system to optimize the allocation of rescuers to the affected areas and the necessary activities in the part of Tehran. The use of combination context-aware and artificial intelligence algorithms for the subject of relief and rescue in earthquake crisis is new research that Led to a 1.79-fold improvement of the proposed solution compared to not considering the existing contexts in relief and rescue without using artificial intelligence algorithms. So can be created a context-aware system based on the appropriate optimization algorithm as a suitable solution to the problem of post-earthquake relief and rescue. Due to the context structure in this research in the individual's activity effectiveness on other individuals and groups, the ant colony algorithm is a collective intelligence base that can provide optimal positioning in a discrete environment. It allows more repetition in less time than other algorithms.}, Keywords = {Context-aware, Optimization, Relief & Rescue, Ant Colony Optimization, Geospatial Information System, Earthquake}, volume = {11}, Number = {2}, pages = {153-162}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1006-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1006-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Hasani, H. and Samadzadegan, F.}, title = {Feature Level Fusion of Hyperspectral Image and LiDAR Data based on Multi-Objective Particle Swarm Optimization in Classification of Urban Area}, abstract ={Hyperspectral and LiDAR data provide spectral and height information and they have high potential in classification of complex urban area. This paper proposed meta-heuristic method in feature level fusion of them. For this purpose, a comprehensive spectral-spatial-structural feature space is generated based on feature extraction method such as spectral indices, texture analysis, roughness, etc. Previous methods apply just one criterion to evaluate classification performance. However, in the proposed method, three criteria including generalization ability, classification complexity and classes separation are considered. Multi-Objective Particle Swarm Optimization (MOPSO) is implemented to select optimum feature space and Support Vector Machines (SVMs) parameters simultaneously while optimize all three parameters. The obtained results show the proposed method increases classification accuracy up to 11% and 58% respect to hyperspectral imagery and LiDAR data by eliminating 300 features (among 611 feature) and also increasing classes separation.}, Keywords = {Hyperspectral, LiDAR, Feature Level Fusion, Urban Area, Multi Objective Metaheuristic Optimization}, volume = {11}, Number = {2}, pages = {163-180}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1012-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1012-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Abdollahzadeh, R. and Vazifehdoost, H. and Vafaienejad, A. R.}, title = {Investigating Effective Indicators in Locating Banks Using a New Geomarketing Model Based on Spatial Data Infrastructure (Case Study: Semnan City)}, abstract ={In today 's world, optimizing a successful business depends on using all the resources that make it superior to its competitors. Location-based marketing or Geomarketing leads to critical and effective decisions by analyzing different geographical areas. Spatial information systems marketing is more agile in strategic decision making. In this age where data dynamics are so important, the use of spatial data infrastructure (SDI) can create a platform for spatial data sharing. Spatial Data Infrastructure (SDI) with instant sharing of spatial data can provide a dynamic platform. SDI-based Geomarketing fixes the flaws and shortcomings of spatial information layers in GIS-based Geomarketing. The main advantage of this model compared to previous models, in addition to information dynamics, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods. This is an applied research in terms of purpose and is based on a descriptive method that includes a set of methods that aim to describe the conditions or phenomena under study. In terms of implementation, part of this research is collected in the form of libraries and documents using the theoretical foundations and background of previous research, and the other part is done experimentally and by collecting information from the base statistical reference authorities.Accordingly, in this research, a new model of location-based marketing is presented, which uses spatial data infrastructure for the first time. In this article, we seek to answer the questions of whether the use of Geomarketing based on spatial data infrastructure has an advantage over GIS-based location-based marketing? Is it possible to prioritize the optimal areas by sharing important indicators from different databases of executive agencies in the field of marketing of Semnan banks? In this regard, using this model and based on the data available in 4 databases of related executive agencies, the city of Semnan is divided into 139 urban areas or statistical areas. Afterwards, using the geoportal infrastructure of Semnan province spatial data located in the Management and Planning Organization of Semnan province, the desired registration information layers were shared and model’s maps were extracted. Subsequently, by examining 150 demographic and economic indicators and examining their correlation coefficient with the number of bank branches, it was found that the indicators of literacy rate, household size, population density, distance from the city center, number of important businesses, income decile, number of apartments and number of schools are most relevant with the number of bank branches in each region. Then a model was estimated using multivariate regression. In this model after estimating the model coefficients, the number of businesses index with a coefficient of 0.598 has the greatest impact on the number of bank branches. According to the results of this model, area No. 62 of Semnan city has the most favorable conditions in terms of banking marketing indicators. So the main advantage of this model compared to previous models, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods in addition to information dynamics. In this model, a dynamic model can be achieved by using dynamic information by sharing layers of spatial information in the context of spatial data infrastructure, in addition to maintaining the intellectual property of information. This research is supported by the GIS unit of the Management and Planning Organization of Semnan Province in Iran.}, Keywords = {Location, Geomarketing, Spatial Data Infrastructure (SDI), Multivariate Regression, Banks, Semnan City}, volume = {11}, Number = {2}, pages = {181-192}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1041-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1041-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Esmaeilzadeh, M. and Amini, J.}, title = {3D Positioning of Targets Using mm-Wave Ground-Based MIMO RADAR}, abstract ={Today, radar systems are used in a variety of applications including remote sensing. One of the most important challenges is to determine the position and the displacement vector of targets with high accuracy. For this purpose, the sensors installed on the ground platforms are used. In this paper, a method is presented that determines the absolute and relative position of the targets and also estimates the displacement of the targets in three dimensions. An FMCW mm-wave ground-based MIMO radar that has two transmitter antennas and four-receiver antennas are simulated. The sensor is designed to move on a two-dimensional plane and received the echo signals which are propagated by transmitters. Using the phase information of the signals captured by receivers antennas in different positions of the sensor, the arrival angle of the signal reflected from the target to the receiver antenna is determined. Then using the least square adjustment equations, the absolute location of the target is determined in the 3D space. Two scenarios were performed to evaluate the proposed model. The first test showed that the RMSE of the absolute position determination is less than 3 mm. in the second scenario, the determination of the displacement vector of the targets is examined. The result shows that the RMSE of the estimated displacement vector in three dimensions is less than 0.1 mm. It was also shown that the proposed method has no limitation in determining the relative displacement with different sizes. The presented model has the ability of the large displacement estimation without using phase unwrapping algorithms.}, Keywords = {mm-Wave Ground-Based RADAR, FMCW RADAR, Displacement, Absolute and Relative Positioning}, volume = {11}, Number = {2}, pages = {193-207}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1005-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1005-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Jahanbani, M. and Vahidnia, M. H. and Aspanani, M.}, title = {Planning to Explore Lime Minerals Using Spectral Angle Mapper (SAM) Processing and Agent-based Modeling (ABM)}, abstract ={One of the advantages of the remote sensing method is that it minimizes surface surveys, especially in inaccessible areas based on spectral information obtained from satellite images. The presence of minerals can be explored by their spectral signatures recorded in satellite images. The main hypothesis of this research is that the combination of such processes with agent-based modeling (ABM) can lead to better planning of the mining exploration and reduce cost and time. In this study, ASTER and OLI sensors were used to identify areas containing minerals in the Yanesar section of Behshahr city. Due to the combination of mineralogy and lithology, the study area is mainly made of lime, shale, clay, and marl. The result of the processing is the exposure of lime units. After pre-processing on the information and satellite images, band ratio methods have been used to identify areas with lime mineral potential. Then, Spectral Angle Mapper (SAM) method was used to more accurately separate these areas using the USGS laboratory spectral library. In order to optimize time and cost in order to identify areas containing lime minerals, agent-based modeling has been used as a new approach. By considering several strategies based on random movement and movement in mineral potential areas, time tables and cost were obtained and compared with each other, and finally, the best results in terms of time, cost, and a number of explorer agents were obtained.}, Keywords = {Minerals, Remote Sensing, Agent-Based Modeling, Netlogo, Spectral Angle Mapper Algorithm}, volume = {11}, Number = {2}, pages = {209-221}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1035-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1035-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Poursoleimani, M. R. and Amerian, Y. and Mahbuby, H.}, title = {GPS and Sentinel-3 Satellite Observations Combination Using Least Squares Collocation Interpolation Method for Tropospheric Delay Model Generation}, abstract ={The main goal of global navigation satellite systems is to determine the precise coordinate of points in all weather conditions. On the other hand, the waves transmitted from the satellites of these systems pass through different layers of the atmosphere such as the troposphere, and this leads to refraction in the path of the wave and ultimately delays in receiving the waves. Therefore, the study of the troposphere and its effects on the signals of the global positioning system is particularly important. The tropospheric delay is divided into two parts, dry and wet, which due to the dependence of the wet part on water vapor changes is the most challenging part of the tropospheric delay.  The aim of this paper is modeling of tropospheric zenith wet delay in Los Angeles in the United States, utilizing a combination of OLCI sensor data of Sentinel-3 satellite with high spatial resolution and data from 97 high temporal resolution GPS stations in this area. The least-squares collocation method is applied for spatio-temporal modeling of the tropospheric zenith wet delay. In order to estimate the trend in the least-squares collocation method, a combination of data from the above two sources was used and a four-dimensional (temporal-spatial) surface was fitted to the tropospheric zenith wet delay data obtained from the OLCI sensor and GPS stations. In the estimation of the observation signal part, instead of considering a spatial-temporal covariance function, the modeling time interval is divided into smaller sub-intervals and the spatial covariance functions are estimated in each sub-interval. The results of the article method and Sastamoinen model which uses the analyzed GFS data were compared with control points values. The RMS of estimated zenith wet delay in control points by least squares collocation was 1.86 centimeter, while the RMS of the Sastamoinen method is 15.97 centimeters.}, Keywords = {Tropospheric ZWD, LSC, GPS, Spatio-temporal Covariance}, volume = {11}, Number = {2}, pages = {223-236}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1038-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1038-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2021} } @article{ author = {Ramezani, A. and Hamedi, Sh. and Hamedi, B. and Ghadimi, A.}, title = {FAT Location/Allocation based on Median and Coverage Problem in a WebGIS}, abstract ={The growing trend of technology and telecommunication industry has led to the use of optical systems to enable the transmission of high bandwidth information in remote locations. Due to the advantages of fiber optic communication platform over copper wire, fiber to home technology has recently emerged and many citizens are applying for fiber optic based internet. Installation and development of fiber-to-the-house technology requires consideration of several parameters, which are location-based and enable the electronic participation of citizens. On the other hand, this technology has expensive equipment that, if not properly distributed, can lead to financial losses of telecommunications and dissatisfaction of citizens. These problems can be reduced by using location-based analysis and a web-based system. The most important innovation of this research is the location and optimal allocation of fiber optic terminal in the form of a web-based spatial system. To optimize FAT location, it will be done in such a way that with the lowest number of FATs, the highest demand (coverage issue) is covered at the shortest distance from the fiber optic joints (middle issue). One of the advantages of the proposed system is the reduction of equipment installation time without face-to-face visits, reduction of telecommunication costs, honoring the client and optimal coverage of the service applicant. The results show that the applicants were 87% satisfied with the mentioned system and the range that was previously covered with two FATs and about 20 working days, was covered with a FAT in 7 working days using the proposed model.}, Keywords = {Optimization, Location,Allocation,FTTH, Citizen Participant, Web GIS}, volume = {11}, Number = {3}, pages = {1-10}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1036-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1036-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Hooshangi, N. and Mahdavi, V. R. and GhaffariRazin, M. R.}, title = {Optimization of Groundwater Level Monitoring Network Using Colliding Bodies Optimization Method (Case Study: Arak Aquifer)}, abstract ={Groundwater level measurement and monitoring are one of the basic and essential steps in groundwater studies. Data in geographical studies, especially in groundwater studies, are generally measured at points in monitoring stations and play an important role in analyzing temporal and spatial variations of phenomena. optimization is one of the main issues in reducing the cost and increasing the quality of the extracted data. The main purpose of this research is to optimize the groundwater monitoring network of the Arak plain aquifer, in which a new metaheuristic method called Colliding Bodies Optimization (CBO) is used. For this purpose, the groundwater wells data are collected and refined. Then the CBO method is implemented and the inverse distance weighting (IDW) method is used to calculate the cost function in CBO. In order to evaluate the accuracy of the output, the results were compared with the ant colony optimization (ACO) method. Arak plain is located in the watershed of Miqan desert wetland and its groundwater level has been decreasing in recent years. Continuous and accurate monitoring of groundwater levels in Markazi province and especially the Arak aquifer is one of its main needs. For this purpose, the groundwater wells data are collected and refined. Then the CBO method is implemented and the inverse distance weighting (IDW) method is used to calculate the cost function in CBO. The CBO algorithm is based on simulating the search space with an environment in which the kinetic energy and momentum of the colliding particles are decreasing. In the proposed approach, using the IDW method, a continuous surface was created from the selected stations, and the error generated in the unselected stations was calculated based on the Root Mean Square Error (RMSE) formula. IDW is one of the simplest spatial interpolation methods that has been used extensively in network optimization. In this study, the average annual data of 57 groundwater level monitoring stations in 1397 were used. Out of 57 monitoring stations, only 43 were active and in 14 stations, measurements were not recorded. Evaluation of outlier data based on Grubbs test showed that well data No. 15 was as outlier data which was excluded from the calculations. In this study, different scenarios were evaluated for removal of 1 to 12 monitoring wells and the curve of the number of removed wells against the amount of error created in the wells was drawn. It was obvious that the error value would increase as the number of deleted stations increased. Comparison of the optimized error percentage shows that the CBO method always has a lower error than the ACO. In general, based on the location of the unselected stations in each scenario, it was observed that the study area is divided into three general parts, north, south, and southeast. In the first three scenarios, the unselected wells are located in the northern part of the aquifer. From scenario four, station No. 6 from the southeastern part is always removed, and from scenario five, station No. 40 from the southern part is always removed as a priority. According to the error diagram, the location of the unselected wells in different scenarios, and also expert opinions, it was found that by removing 6 wells (wells with numbers 5, 6, 9, 30, 36, and 40) from the groundwater monitoring network of Arak aquifer, a maximum of 35 cm of accuracy will be reduced in the well. On the other hand, it saves money and time for data collection. The location of the unselected wells shows that the wells around Miqan Wetland are of great importance in estimating the groundwater level of the aquifer and most of the removed wells are located in the northern and central part of the aquifer. This study also showed that the object collision optimization method is a suitable method in optimizing groundwater monitoring networks.}, Keywords = {Monitoring Network, Groundwater, Colliding Bodies Optimization (CBO), Metaheuristic Method, Inverse Distance Weighting (IDW)}, volume = {11}, Number = {3}, pages = {11-23}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1018-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1018-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Saeedi, R. and Aghamohammadi, H. and AlSheikh, A. A. and Vafaienejad, A. R.}, title = {Development of a Method based on MobileGIS and VGI to Improve Relief for Victims in Times of Crisis}, abstract ={Due to the value of time in disaster relief, access to current information about the geographical location and physical condition of the injured and the use of new technologies and methods of GIS is important. The purpose of this study is to create a platform that uses MobileGIS, WebGIS and VGI technology to transfer and collect immediate and accurate information that leads to reducing relief time, increasing the speed of sending online or offline assistance requests by victims and reducing casualties. Real-time information on the physical condition and position of the injured person according to the required hardware facilities and the importance of time in responding to the needs of the injured is provided by each victim and used in this system .In this research, the information sent by the victims and those around them has been used. The information sent in the geoserver is analyzed .At the disaster, due to solving the problem of disconnecting the Internet, the output of this research was used as a mobile application with Android and IOS operating systems. In addition, the web version of the system was available when connected to the Internet. After the implementation of this system, with the obtained information, an Instant map was made available to the rescue manager, which included mapping the type of assistance and relief for each person or in groups for a group of injured in a specific geographical area, which help to assistance-and-relief managerschr('39') decision-making process.}, Keywords = {Disaster Management, WebGIS, SMS, MobileGIS, VGI}, volume = {11}, Number = {3}, pages = {25-36}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1047-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1047-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Sharifi, M. A. and Shirafkan, Sh. and Khazraei, S. M. and AmiriSimkooei, A. R.}, title = {Short-term Prediction of the LOD Time-series using a Combined SSA+ARMA Method}, abstract ={The Earth Orientation parameters (EOP) including the Length of Day (LOD) are vital to widely used applications of Satellite Geodesy. The precise satellite positioning and navigation and the Earth system monitoring satellites are a few applications with a near real time request for the EOP values. Data gathering from a globally distributed co-located geodetic sensors and processing the collected data for estimation of the parameters are time demanding tasks which makes delayed access to a real-time processed values unavoidable. Consequently, accurate prediction of the EOP parameters time-series has been defined as a highly demanding task in geodesy. Many different techniques have already been employed either for short- or long-term prediction of the time-series. However, the implemented methods for short-term prediction of the EOP temporal changes is in central focus of the research centers and institutes due to its applications in the Earth Centered inertial (ECI) and the Earth Centered Earth Fixed (ECEF) reference frames. Moreover, combined methods with the ability of simultaneous functional and stochastic behavior modeling of the time-series are more interested due to their functionality for one-step accurate prediction. For instance, the Least squares auto regressive (LS+AR) is the most recent published article for the LOD forecasting. In this paper, we will address an innovative approach for complicated and challenging time-series prediction. The proposed methodology consists of the Singular Spectrum Analysis (SSA) combined with the Auto Regressive Moving Average (ARMA) enabling to model the functional and stochastic constituents respectively.  For more precise forecasting, the proposed method is equipped with two pre analysis statistical tests in order to detect and identify any possible outliers. Moreover, the Fast Fourier transform (FFT) is employed to give a first guess of the possible periodic pattern of the data with its later application in the SSA appropriate window length selection. The SSA setup consists of the lag-covariance matrix computation, Eigen value and Eigen vector decomposition of the lag-covariance matrix, the Eigen values clustering and the component reconstruction. Trend and offset removal before utilizing the SSA method and its restoration after performing perdition is also worth mentioning. Selection an optimal number of deterministic components plays a key role in effective implementation of the SSA approach which is fully explained in the methodology part of the paper. The stochastic behavior of LOD signal is characterized using the ARMA technique whose successful implementation is highly depend on the right selection of  the Moving Average (MA) and Auto Regressive (AR) orders. The Akaike information criterion (AIC) as an estimator of prediction error as the well-known order selection criteria is used. Moreover, the Mean Absolute Error (MAE) is computed and different prediction scenarios are compared. The suggested approach has dominantly outperformed  eight already published method. Publicly available LOD data from International Earth Reference System (IERS) is used for the method evaluation and numerical comparison. Daily LOD data for the years of 2005-2009 is used for model training while the year 2010 is taken for validation. A ten-day interval prediction during the whole year of 2011 is considered for evaluation. On average, accuracy improvement rate is about 1.6 and 1.84 for the 5th and 10th ahead day of prediction.}, Keywords = {LOD, SSA, ARMA, Prediction}, volume = {11}, Number = {3}, pages = {37-49}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1064-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1064-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Bagheri, S. and Karimzadeh, S. and Feizizadeh, B.}, title = {Investigation and Modeling of Physical Growth of Urban Areas and its Impacts on Traffic using Night-time Light Data}, abstract ={Today, the rapid physical growth and development of cities has caused significant changes in their physical and functional characteristics, and as a result, many problems have arisen. The cities of Tehran and Tabriz, as the two metropolises of Iran, are no exception to this rule, because the reason for their development in recent years and the concentration and overcrowding of various uses, especially commercial uses and medical services in the central sector, issues and problems for transportation network failure. It has created two cities. Accordingly, in the present study, these two metropolises have been studied. Remote sensing observations at night provide us with an explicit and timely measurement of human activities. Numerous studies have shown that night light (NTL) can be used as a proxy for a number of variables, including urbanization, density, and economic growth. Accordingly, in this study, we examined urban growth and its effects using remote sensing at night. For this purpose, satellite images of SOUMI NPP, LANDSAT 8 and LANDSAT 7 as well as traffic information obtained from Google map were used, using ENVI 5.3, QGIS 3.10, ARC GIS 10.3 software, Google Earth Engine system and MATLAB software. This data was done. First, the physical development of the studied cities was investigated using the BUNTUS algorithm (urban built-up areas, night light image and travel time for the city limits). The results showed that both cities had a slight slope growth during this nine-year period (2020-2012). After calculating urban growth to study urban traffic, regression was performed between traffic information and numerical value of image pixels (DN) using MATLAB software, which showed the correlation between these two layers of information.}, Keywords = {SOUMI NPP , LANDSAT 8 , LANDSAT 7, BUNTUS Algorithm}, volume = {11}, Number = {3}, pages = {51-61}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1048-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1048-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Badrloo, S. and Varshosaz, M.}, title = {Detection of Obstacle Regions Around an MAV using an Expansion-based Technique}, abstract ={Introduction Micro-Aerial Vehicles (MAVs) are ideal platforms for indoor and outdoor applications because to their small size and light weight [1, 2]. Obstacles, on the other hand, may cause MAVs to crash. Cameras collect a lot of evidence about their surroundings. Through the use of grayscale values [3], point features [4], and edge details [5], vision-based techniques detect obstacles. They are divided into monocular and stereo types. There are four types of monocular approaches: appearance-based [6], motion-based [7], depth-based [8], and expansion-based [4]. Expansion-based techniques are based on the same principle as human vision. The majority of expansion-based systems identify obstacles by recognizing object points [4, 5, 9-11]. However, relying solely on points may not be sufficient; as a result, the MAV may collide with unseen impediments. To overcome this obstacle, we describe a novel technique based on the same concept but employing region-enlarging rates. Methodology Various steps of our obstacle detection technique above can be summarised in (a) data acquisition and preparation, (b) region extraction and their area calculation, and (c) obstacle detection. Results and discussion We took four pairs of images with an LG 360 CAM fisheye camera, two with the camera moving forward and two with the camera moving to the sides. In the forward direction, recall accuracy is 82% for the first data and 52% for the second data. The new technique detects only a portion of the obstacle region. This problem emerges because some regions lack at least three matching points. While moving to the right, recall accuracy for the third and fourth data is 69% and 39%, respectively. This accuracy is lower in the fourth data set than in the other data sets due to the aforementioned description, the absence of at least three corresponding points, and the possibility of inaccurate corresponding points, particularly along the fisheye image's edges, which have a low quality in these places. Conclusion                   The proposed method extracts regions of close obstacles from outdoor fisheye images. The findings demonstrate the method's efficacy in a variety of complex environments. Thus, on average, 60% of the obstacles are detected in two modes of forward movement and right movement. Additionally, a comparison of the suggested method to that of Al-Kaff et al. (2017) [4] demonstrates that it is more efficient than the existing algorithm. The proposed algorithm, however, has some gaps in terms of obstacle detection.One of these limitations is that certain regions lack at least three corresponding points. Also, the presence of incorrect corresponding points causes incorrect detection of obstacles. The second limitation is that the obstacle is the same color as the background, which leads to errors in the correct detection of obstacles. The third constraint is the long processing time required by the suggested approach. These constraints can be solved in the future with the use of more accurate and faster algorithms.}, Keywords = {Obstacle Detection, Regions, Expansion-based Method, MAVs, Fisheye Image}, volume = {11}, Number = {3}, pages = {63-81}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1030-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1030-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {DavoodAbadiFarahani, M. H. and Sharifi, A. R. and Arabi, M.}, title = {Monitoring of Agricultural Drought in Markazi Province using VHI and PDSI Indices}, abstract ={One of the natural disasters of all is drought. Drought has long been one of the problems of Iran and has always been mentioned as a serious threat to the country. Drought is one of the natural and repetitive features of the climate and part of the climate that cannot be observed without a specific limit of occurrence and impact using high-speed ground station information. Drought rate is divided into severe, mild, moderate drought and long-term drought and short-term drought based on time components. Drought has had effects on land degradation, forest fires, reduced air and water quality, and reduced agricultural production. Frequent droughts have been a concern for many years, and on a global scale, in recent decades, especially in arid and semi-arid regions, the frequency, severity and duration of droughts have increased significantly. Drought according to different definitions in each area is divided into four main categories, including meteorological drought, agricultural drought, hydrological drought and socio-economic drought. In meteorology, drought, or in other words dry period, is the reduction of loss in a period of time compared to the average amount of rainfall in that period or the amount of long-term rainfall in the same period. Of course, if this amount of rainfall does not meet the needs such as economic and social needs. The duration or period of drought varies according to the climate of each region and depends on the location and time. In Iran, the drought period is approximately equal to the crop year. The severity of drought also varies in each climate and country. The same amount of rainfall in one country may be considered drought, while in another country, the same amount of rainfall does not indicate drought. Agricultural drought damages the economy, social conditions, agricultural products and consequently food security, so monitoring it is essential. For this purpose, agricultural drought in Markazi province has been studied. One of the factors affecting agricultural drought, soil moisture and plant evapotranspiration as a cause of water loss is the difference between the available water and its wastage. Therefore, to measure drought, an indicator that shows evapotranspiration is needed. In this research, RWDI index is used as an indicator of drought based on evapotranspiration. To obtain this index, one must first obtain the actual evapotranspiration and potential. Due to the advantages of remote sensing methods compared to traditional methods, remote sensing methods based on energy balance and SEBAL2 image processing model as well as Landsat 8 images, in dry and wet seasons, along with meteorological data to obtain heat flux Hidden, which depends on the parameters of soil heat flux, tangible heat flux and net radiation flux, is used. Using latent heat, instantaneous evapotranspiration is calculated daily and using the Penman 3 method, reference evapotranspiration is calculated. Potential method of Taylor 4, evapotranspiration and potential transpiration are obtained, and as a result, after calculating the actual evapotranspiration, the RWDI index is prepared, which shows the water shortage and the severity of agricultural drought. The results show that the values ​​of evapotranspiration are obtained with acceptable accuracy. Meteorological stations are obtained in REF-ET software. In addition, based on the results of the water deficit index, the values ​​of this index are expected to be higher in dry seasons than in wet seasons.  }, Keywords = {Agricultural Drought Monitoring, VHI, PDSI}, volume = {11}, Number = {3}, pages = {83-100}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1028-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1028-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Minaei, M. and Malek, M. R.}, title = {Smart Camera-based Contact Tracing Systems: A Review}, abstract ={With the spread of the COVID 19 virus and the threat of a pandemic, the World Health Organization (WHO) proposed quarantine and setting social distancing restrictions as a means of disease management. Despite their effectiveness, implementing and administering these restrictions is prohibitively expensive and not suitable for a long time. Contact tracing systems can be used as an alternative to public quarantine. Smart cameras are an easily accessible structure for developing such a system due to their widespread use. However, there are numerous challenges in designing and implementing the mentioned system. The architecture of an effective object detection model, a large enough dataset, and a method for positioning and tracing people are some of the challenges that have been examined and compared in this article.}, Keywords = {Contact Tracing, COVID 19, Smart Cameras, Object Detection Algorithms, Image Processing}, volume = {11}, Number = {3}, pages = {101-113}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1053-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1053-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Tajfirouz, B. and Saeidi, V. and Khalili, H. and Nemati, M. H.}, title = {Volume and Rate Estimations of Sedimentation in Amirabad Port During Period 2018-2021}, abstract ={More than 70 percent of the Earth is covered by water, and water bodies are the main features in the globe. Hence, studies of water depth and bathymetry are of great importance. Hydrography is the most accurate method in bathymetry science to measure the seafloor surface and water depth for defense and security, development, and environmental purposes. Mainly, maritime transportation and safety rely on spatial information databases and accurate hydrography of the seafloor in ports and coastal areas as well as periodic sedimentation monitoring in ports and approaches channels. Regular sedimentation monitoring allows for sedimentation distribution mapping, precise maintenance dredging, and safe navigation in coastal zones. To determine spatial and temporal trends of sedimentation, periodic hydrography of coastal areas is in demand. Therefore, in the present study, sedimentation rates were estimated based on precise hydrographic datasets in the port basin and approaching channel of Amirabad port (one of the most important Iranian ports in the Caspian Sea) in the period 2018-2021. Using the Ceeducer Pro (a 200 kHz hydrographic survey system) in 10 separate hydrographic surveys (with 2 to 6 months intervals), the three dimensional (3D) datasets were acquired with accuracies of higher than 0.5 m. After data processing (based on different dates), the digital surface models of the seafloor were produced for every hydrographic surveying. Then, the sedimentation volumes were calculated and compared in pairs. To estimate the final volume of the sedimentation by considering the dredging operations in the study area, the amount of sedimentation transferred by the dredging processes was also calculated and added to the final volume of the sedimentation. The outcomes confirmed the average rates of annual and monthly sedimentation (m3 / m2) in the channel by 0.122m and 0.010m and in the basin by 0.160m and 0.013m, respectively. Generally, a homogeneous distribution in sedimentation was observed in the whole region. However, a pattern of the highest volume of sedimentation occurred in the entrance of the channel (in the north and northeast direction), in the south of the main basin, and the western and eastern parts of the basin. Moreover, the highest rates of sedimentation were predominantly observed in the warmer seasons in Amirabad port. According to the finding of this research, the maintenance dredging system was performed at a satisfying level, resulting in successful removal of sediment from the seabed in the study area. Besides, undertaking a maintenance dredging system at longer intervals (i.e., more than four months) might threaten the navigation safety of ships in the region. The results of this research will help private and government sectors for more accurate planning and optimal dredging of the region in the future. In addition, these datasets are reliable and accurate reference and source for the validation and evaluation of other common empirical and semi-empirical methods in estimating sedimentation rates in Amirabad port.  }, Keywords = {Hydrography, Sedimentation Rate, Dredging, Amirabad Port, Maritime Safety}, volume = {11}, Number = {3}, pages = {115-123}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1054-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1054-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Gholamnia, M. and Ghadimi, M. and Moghbel, M. and Khandan, R.}, title = {Diurnal Temperature Cycle Analysis in Various Land types and Comparison with MODIS Land Surface Temperature}, abstract ={Land Surface Temperature (LST), as a key parameter in environmental interaction, has been studied in several researches. Urban thermal condition depends on land cover types that composed of different materials with various thermal properties. In this study we analyzed diurnal temperature of vegetation, stone, water, cement, asphalt, and soil land cover types (LCT), as main components of urban structure. The Diurnal Temperature Cycle (DTC) method used to model daily behavior of different material temperature’s by implementation of precise sensors at a weather station in Tehran, of Iran. Then, comparisons between maximum diurnal temperatures of different LCT with LST at the day and night time of MODIS were conducted. Result showed that in warm days the discrepancies between materials were larger and soil, asphalt, and cement had higher temperatures than stone, vegetation, and water. Also, water had little fluctuations and some phase shift to reach maximum amplitude. Also, time series of MODIS LST in the study area were extracted and compared with maximum diurnal temperature of different LCT. Maximum correlation was estimated between Terra daytime LST and T_max of soil and cement material with 0.948 values for "R^2"  and 2.89 and 4.2^C"  for RMSE.}, Keywords = {Maximum Temperature, Urban, MODIS, LST, Diurnal, Land Cover}, volume = {11}, Number = {3}, pages = {125-132}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1011-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1011-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {PeiroHosseiniNejad, M. and Karami, A.}, title = {Automatic Panicle detection in unmanned aerial vehicle images using TSDPC}, abstract ={Panicle counts (PC) provide valuable information about yield prediction in sorghum but are expensive and time-consuming to acquire via traditional manual approaches. In this thesis, high-resolution RGB imagery acquired by UAVs has been used. The proposed method based on task-aware spatial disentanglement (TSD) has been modified to improve the performance of panicle detection. TSDPC has high accuracy in comparison to state-of-the-art techniques such as CenterNet and RepPoints.}, Keywords = {Deep Learning, UAV Images, Panicle Counting, Small Objects}, volume = {11}, Number = {4}, pages = {1-10}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1056-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1056-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Moharrami, M. and NeysaniSamany, N.}, title = {Comparative assessment of Deep Learning and Random Forest methods for urban land cover classification (A case study Tabriz city)}, abstract ={Rapid urban growth, especially in developing countries, is causing a large number of urban planning problems. Although only three percent of the global land surface is covered by urban areas, approximately 54% of the world’s population lives in urban centers; according the latest estimates, by 2050 it will increase to nearly 65%. Accurate information on Urban Land Cover (ULC) types and their spatial distribution are of paramount importance for urban planning and management. To date, many studies have been conducted in the context of ULC mapping, and several methodologies and datasets have been used (e.g. land surveying and satellite data) in this regard. Under this background, generating ULC maps using land surveying method is considered as the most accurate technique, however, it is a costly and time-consuming task. Spending the least time and cost to produce these maps is one of the main challenges for city managers. To address this issue, the integration of satellite images and state-of-art classification methods has been received considerable attention in recent years. This study seeks to produce a 10 m resolution ULC map for Tabriz city, locating North East of Iran, using Sentinel-2 satellite data. The present study also aims to compare the potential of two advanced classifiers including Random Forest (RF) and Deep Neural Network (DNN) in ULC mapping. Five ULC classes including bare land, built-up areas, road, vegetation, and water were considered in this regard. As the number of trees (ntree) and the number of variables (mtry) are two main criteria applying the RF algorithm. In this study, ntree was set to 100 and the mtry was set to the square root of the total number of input features. In the case of DNN, a DNN model with six layers, including one input layer with 10 neurons (bands 2-8A and 11-13 of sentinel-2), four hidden layers with 200 neurons per layer, and one output layer (five ULC classes). In this study, the ReLU activation function was used for the hidden layers, softmax activation function was used for classifying information in the output layer. Our findings illustrated that the DNN algorithm by providing 95.2% overall accuracy outperformed RF (overall accuracy = 93.1%). Analyzing the performance of two algorithms regarding ULC classes showed that the DNN algorithm provided better results in bare land and built-up classes; the user’s accuracy and producer’s accuracy of bare land class were respectively 9.6% and 1% higher than those of RF. Regarding the built-up class, these metrics were also higher than RF (user’s accuracy = + 0.3% and producer’s accuracy = + 4.3%). In contrast, the RF algorithm performed better in extracting the road class; the user’s accuracy and producer’s accuracy of road class were 3.65% and 4.1% more than those of DNN, respectively. RF and DNN showed the same performances in classifying vegetation and water classes. In general, both algorithms provided good performances in ULC classification, however, the overall performance obtained by the DNN algorithm was substantially higher than RF. Because the performance of the DNN algorithm is better than the RF algorithm, we concluded that DNN is a valid alternative tool that should be considered for ULC mapping.}, Keywords = {Urban Land Cover, DNN, Random Forest, Sentinel-2}, volume = {11}, Number = {4}, pages = {11-23}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-973-en.html}, eprint = {http://jgst.issgeac.ir/article-1-973-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {SadeghiPaland, R. and EbrahimianGhajari, Y.}, title = {Site Selection of Temporary Flood Resettlement Centers Based on Integration of Multi-Criteria Decision-Making Methods and Optimization Algorithm (Case Study: Mazandaran Province)}, abstract ={The selection of an optimal and appropriate place for temporary accommodation of people affected by natural disasters, such as flood, has long been of concern to Crisis Management Planners. Failure to properly locate these centers may result in heavy damage. The objective of the present study is to determine appropriate places for establishing temporary post-flood settlement centers in Mazandaran province. In order to achieve this objective, firstly, effective criteria for locating temporary accommodation were determined and the standard maps were prepared and normalized. In the present study, nine criteria for locating temporary accommodation centers have been applied. The Analytic Hierarchy Process (AHP) method has been applied to weigh the criteria by availing experts’ opinions and studying related articles. In the next step, in order to incorporate the criteria according to the weights calculated by the method of AHP, the method Weighted Linear Combination (WLC) was applied and the final map was categorized into four classes of “very appropriate”, “appropriate”, “not appropriate” and “very inappropriate”. In conclusion, the location of temporary post-flood settlement centers with respect to demographic points was considered in the two classes of “very appropriate” and “appropriate” from the previous stage using the P_Median function in the genetic algorithm. The algorithm searches for 15 locations among the candidate points for temporary accommodation. The centers are located in higher populated areas in two classes more desirable than other classes.}, Keywords = {Temporary Sheltering, Flood, GIS, AHP, GA, P-Median}, volume = {11}, Number = {4}, pages = {25-37}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1065-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1065-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Sharifi, M. A. and Shahriarinia, K. and Shirafkan, Sh. and Khazraei, S. M. and AmiriSimkooei, A. R.}, title = {Short-term Prediction of Polar Motion Parameters Using Deep Neural Networks}, abstract ={There are many instabilities in the earth's rotation due to celestial bodies, gravitational forces, and earth internal dynamics which make the calculation of Earth Orientation Parameters (EOP) and Polar Motion (PM) parameters a challenging task. In today's world due to the increasing requests for predicting EOP and PM parameters in a wide range of fields such as Astronomy, Geodesy, Oceanography, and Hydrography various methods are used. These days it is possible to calculate accurate values of EOP and PM parameters by means of global positioning system (GPS), very-long baseline interferometry (VLBI) and satellite laser ranging (SLR). The core reason for the short-term prediction of these parameters is the impossibility of calculating these parameters in real-time due to heavy preprocessing procedures. Hence, researchers are seeking to employ different methods for accurate short-term prediction of EOP and PM parameters. In recent years, non-parametric methods such as least square (LS) with autoregressive moving average (ARMA) and also singular spectrum analysis (SSA) have been used to estimate these parameters. Another method for the prediction of the aforementioned parameters was conventional artificial neural network (ANN). Currently, Deep Learning has become a popular field that attracts many researchers. Deep learning is a subset of artificial intelligence (AI) and machine learning that uses multiple layers and parameters in order to extract complex features from the inputs. It is widely used in computer vision and time series prediction applications. In this paper, we used three deep learning methods namely LSTM, CNN, and MLP in order to predict PM parameters (x and y parameters). Furthermore, we have used Least square harmonic estimation (LSHE) method in order to compare the final results with different networks. LSTM equipped with a short-term recursive memory. This recursive mechanism prepares LSTM for handling time series data. CNN extracts important features of input data by convolution multiplication and each convolution layer within CNN architecture produces feature maps as output. These Feature maps contain recognized patterns which will be used as input of next layer. CNN networks, which were primarily designed for the computer vision, are being used more and more in timeseries prediction applications. MLP networks are similar to conventional back propagation feedforward networks, however, new activation functions and optimizers could be used in MLP networks. For sufficient training of different architectures, we used 35 years of daily PM parameters from 1st January 1980 to 31 December 2015 and we predicted 40 days periods ahead for the future 5 years. For comparison of predicted values by different networks, we used mean absolute error (MAE) as a criterion and illustrate results in two tables. Also, we depicted different figures to show how networks are working. In addition, we used two LSTM, four CNN, and two MLP Networks with ADAM optimizer, ReLU activation function, and learning rate of 0.001- 0.0001 in order to select the best network with the lowest errors.  Moreover, the figures of predicted values vs actual values and plots of MAE for 40 days are shown on four figures for better comprehension of ultimate results. In the end, it turned out that LSTM networks outperformed CNN and MLP networks and in this network the final results are better than the others in most days. For the x parameter in the first, twentieth and fortieth days, the best MAE values are 0.41 mas, 5.58 mas, and 12.45 mas and the best values for RMSE are 0.49 mas, 6.69 mas, 15.05 mas, respectively. For the y parameter in the first, twentieth and fortieth days, the best values of MAE are 0.54 mas, 3.24 mas and 7.56 mas and the best values for RMSE are 0.68 mas, 4.72 mas, 9.22 mas, respectively. The final results show that the neural networks outperformed LSHE method and the accuracies of the deep learning networks are satisfying and LSTM and CNN networks are capable to predict values accurately.}, Keywords = {Timeseries, Polar Motion, Deep Learning, LSTM, CNN, MLP}, volume = {11}, Number = {4}, pages = {39-53}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1068-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1068-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Mohammadi, M. and Sharifi, A. R.}, title = {Oil spill detection using Sentinel-1 and Landsat-8 images}, abstract ={Environmental pollution and disasters have gradually increased with population growth. The presence of oil resources in the seas and the incidents related to their discovery, extraction and transportation cause the formation of oil slicks on the sea surface, and the leakage of these petroleum products into the seas has irreparable environmental consequences. That is why monitoring the effects of these accidents is very important for public health. Satellite missions are a very effective tool for detecting pollutants such as oil spills. Artificial Aperture Radar Sensor (SAR) is an active microwave detection system that can be used to detect oil leaks with optical sensors installed on Landsat-8, Sentinel-2 and Ester satellite systems, taking into account cloud cover and satellite re-visit time at the same location. Be. In this study, the oil spill area due to pipe leakage in Khark Island was studied with Landsat-8 and Sentinel-1 satellite images. Various image processing techniques were applied to Landsat-8 bands to highlight oil spills in connection with the accident, such as morphology and convolution filters. We used Landsat-8 images to support the Sentinel-1 results. Oil spills were successfully detected by analyzing SAR data and Landsat-8 results, and by visually interpreting the results, the selected methods are consistent in terms of displaying oil spill areas.}, Keywords = {Oil spill, Sentinel-1, Landsat-8, image processing}, volume = {11}, Number = {4}, pages = {55-65}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1029-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1029-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Zeaieanfirouzabadi, P. and Sheikhghaderi, S. H. and Kelarestaghi, M.}, title = {Utilization of a deeply Refined Deep Residual Convolutional Neural Network to evaluate and compare the accuracy of Road detection from Sentinel 1 radar images (Case study: Tehran and Shiraz metropolises)}, abstract ={In recent years, Road detection and road extraction from satellite images with the advancement and development of deep learning algorithms in the field of semantic segmentation has received more and more attention of researchers. In this regard, most of the studies have been done in the field of Road detection and road extraction using optical images and in these studies, few studies have been performed using radar images worldwide. Therefore, the aim of this study was to use a deeply Refined Deep Residual Convolutional Neural Network (RDRCNN) to evaluate and compare the accuracy of road extraction from Sentinel 1 radar images in Tehran and Shiraz metropolitan areas in equal conditions in terms of number of educational samples, validation and architecture. It is the same. In this study, to extract the road using DNN, the VV-VH color combination of Sentinel 1 radar images from 8 different cities (Tehran, Mashhad, Isfahan, Shiraz, Tabriz, Urmia, Baghdad and Beijing) was used. Finally, the RDRCNN model with a residual connected unit (RCU) and a dilated perception unit (DPU) was used for road training and extraction. The research findings indicate that the RDRCNN model has performed almost the same in the process of identifying and extracting roads in the two cities of Tehran and Shiraz, and in general, the above model has performed slightly better in the city of Shiraz. In terms of accuracy evaluation metrics, for Tehran images, the criteria were Recall 57.66%, accuracy 51.29%, F1 score 54.43% and overall accuracy 92.78%, and for Shiraz images Recall criteria 60.77%, accuracy 54.71%, F1 score 57.40% and overall accuracy of 95.63% were obtained. The findings of this study show the low accuracy of road training and extraction from Sentinel 1 radar images for two metropolitan areas of Iran. In general, by comparing the results of this study with previous studies, it can be seen that one of the most important reasons for the low accuracy of the results is the low width of roads in Iranian cities; However, due to the lack of necessary studies in the field of road extraction with Sentinel 1 radar images, it is not possible to comment definitively on the results and it is suggested that more studies be done in this field.}, Keywords = {Deep Learning, RDRCNN, Sentinel 1, Road Extraction, Tehran, Shiraz.}, volume = {11}, Number = {4}, pages = {67-82}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1062-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1062-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {AliAslKhiabani, E. and ValadanZoej, M. J. and Maghsoudi, Y.}, title = {Monitoring the earth-fill dams displacement by using the time series radar data (Case study: Mamlu dam)}, abstract ={With increasing the number of large engineering structures in cities, experts are looking for a good solution for monitoring these structures to avoid great financial and human damages. From the past, leveling and ground surveying were carried out to measure the deformation of structures and ground displacements along the vertical direction; but these measurements are time-consuming and costly. Also, the using of precision instruments and deformation sensors are not suitable because of their high cost, time-consuming and complexity. Due to the ability of Radar images and Radar interferometry techniques in the field of monitoring the ground displacement, in this research, we are looking for evaluating the potential of this method for monitoring the dam deformation and displacements. To achieve this goal, we used two sets of radar data which are CosmoSkyMed-X and Sentinel-1A.   In the time series processing of these images, the PSI method was selected then the star graph and Deloney triangulation were used. In the next step, we used both linear and nonparametric models for displacement estimation. The results were evaluated by applying two different displacement models and finally, the model with higher temporal coherence was selected as the appropriate model and the other model was discarded. In processing the Mamlu dam images, the appropriate model for monitoring the displacements with S-1A Radar data was the nonparametric model and for CSK data was linear model. Due to the lack of ground data collection from Mamlu dam in the same period of time with radar data, to evaluate the accuracy and proof the obtained results, the results of two radar data sets (CSK and S-1A) were compared with each other and a very high agreement was observed between the results of these data sets that the amount of RMSE calculated for ASC data of these two sensors is equal to 0.7703 mm and for DSC data was calculated 0.9551 mm, which is a very high consistency of the results, which can be a reason for the accuracy of the extracted results.}, Keywords = {Radar interferometry, Persistent scatterer technique, Earth-fill dam, CosmoSkyMed-X images, Sentinel-1A images}, volume = {11}, Number = {4}, pages = {83-96}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-969-en.html}, eprint = {http://jgst.issgeac.ir/article-1-969-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {ZamiriAghdam, F. and Akhoondzadeh, M. and DehghaniJabbarlou, M.}, title = {Monitoring of Urmia Lake Bridge Subsidence during 2014- 2021 Using DInSAR-SBAS Method and GPS Data}, abstract ={The Differential Interferometric Synthetic Aperture Radar (DInSAR) can be considered an efficient and cost-effective method for monitoring ground subsidence because of its extensive spatial coverage and high precision. Because of orderly observations from a broad-range product, the new commissioning of the first Sentinel-1 satellite offers better support to operational scrutinies via DInSAR. In the present paper, the results of a Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) time-series analysis of 42 Interferometric Wide swath (IW) products of Urmia Lake Bridge in northwestern Iran acquired between November 2014 to April 2021 for both descending and ascending pass using the Sentinel-1A observation with Progressive Scans in azimuth (TOPS) imaging mode is studied. The SBAS processing was based upon the analysis of 111 small-baseline differential interferograms. The results demonstrate that the majority of regional ground subsidence rates in the research area ranged from 10 to 210 mm during the study period. Also, the maximum subsidence rate exceeded 210 mm/year. The Line of Sight (LOS) direction for descending pass is 91 mm/year for ascending the pass. The board view displays that ground subsidence is intense on the bridge. The largest subsidence center is located at the central points of the bridge. GPS data verified the SBAS-InSAR-derived result. The results include displacement in the horizontal and vertical directions for ascending and descending passes.}, Keywords = {InSAR, Sentinel 1, Subsidence, SBAS, SARscape, Interferogram}, volume = {11}, Number = {4}, pages = {97-105}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1044-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1044-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Bahrami, P. and Moradbeygi, A. and Haeri, Z. S.}, title = {Assessing the success of the implementation of provincial spatial data infrastructure (PSDI) with the combined approach of DEMATEL and Network Analysis Process (DANP) (Case study: Ilam province)}, abstract ={Spatial data infrastructure is a set of policies, standards, access networks, spatial data, organizations and people that facilitate and coordinate the various tasks of production, collection, storage, access and optimal use of spatial data in a specific area. In order to implement the provincial spatial data infrastructure, the provincial management and planning organization will be responsible for leading and coordinating between the executive organs of the province and the establishment of the provincial geoportal. In order to measure the success of spatial data infrastructure implementation in Ilam province, first the effective factors and indicators in the successful implementation of provincial SDI were identified and then weighed using the combined method of Dematel technique and network analysis process (DANP), which the indicators of structure, financial resources, specialization, education and culture, respectively were the most important. Then the implementation of each of the criteria in the province was examined. Finally, the success rate of spatial data infrastructure implementation in Ilam province was calculated. The results showed that the realization rate of SDI in Ilam province is 63%, which according to the existing conditions, the performance of this province can be at a good level. Finally, the weaknesses in the implementation of SDI in Ilam province identification and solutions to eliminate them and increase the success rate of SDI in this province were expressed.}, Keywords = {Spatial Data Infrastructure (SDI), Demetel and Network Analysis Process (DANP), Ilam Province}, volume = {11}, Number = {4}, pages = {107-117}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1067-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1067-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Einali, M. and Alesheikh, A. A. and Atazadeh, B.}, title = {Mapping registration boundaries in 3D using Building Information Modeling in the context of Iranian jurisdiction}, abstract ={In recent years, the population in Iran has been increasing. This growth in the population of large cities multiplies the demand for dwellings. The increase in demand, in turn, has led to the creation of high-rise buildings and numerous complex registration maps in these buildings. The current method for registering and storing various legal boundaries in Iran is based on two-dimensional maps. This method has its limitations for visualizing different registration boundaries that define legal spaces. For this reason, a new method to register the ownership of buildings accurately and efficiently is more than ever needed. With the advancement in the field of 3D modeling, especially Building Information Modeling, a lot of research is being done to record the three-dimensional legal boundaries using these models in different countries. In this article, for the first time, Building Information Modeling was used to record three-dimensional legal boundaries in the Iranian jurisdiction. To do this, first different types of registration boundaries in Iran were identified according to the regulations of the Real Estate Registration Organization of Iran and the building subdivision instructions in Iran. Then, to record these boundaries, Industry Foundation Classes (IFC) was developed and implemented on a prototype, which is a building in Tehran. In the evaluation stage, the registration boundaries in the two proposed methods and the current method were compared using a questionnaire. The results of this evaluation in different criteria showed the high capability of the proposed method in registering and visualization legal boundaries approved by the Real Estate Registration Organization so that more than 80% of the elite community participating in the questionnaire considered this method more appropriate than the current method.  }, Keywords = {property registration, 3D cadastre, Building Information Modeling, IFC, 3D data model}, volume = {11}, Number = {4}, pages = {119-130}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1076-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1076-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} } @article{ author = {Fathollahi, M. and Soosani, J. and Mohammadzadeh, A. and Puttonen, E. and Hosseinzadeh, R.}, title = {The efficiency of TOF technology in smartphones to estimate the diameter of some Hyrcanian forest index trees}, abstract ={The lack of efficient inventory tools is an old and well-known problem related to in situ forest measurements. Time of flight technology (TOF), using photogrammetric techniques and the ability to detect depth, allows the creation of 3D point clouds and the measurement of various objects, including tree stems. Therefore, this paper presents the accuracy of diameter measurement with TOF technology compared to calipers for six tree species: (Fagus orientalis Lipsky), (Quercus castanaefulia C.A.M. subsp), (Acer velutinum Boiss), (Carpinus betulus L.), (Alnus subcordata C.A.M.), (Parrotia persica C.A.Mey.), differing in stem shape and bark (20 of each species) were studied in the Darabkola Research Forest. Then, the diameters at a height of 1 and 1.30 metres of the tree trunks were measured with a vernier calliper and a point cloud was created using a Phab 2 Pro smartphone. The target diameters were measured using the CloudCompare software. The results showed that there was not much difference between the field recordings and the TOF measurement. However, Acer velutinum, Fagus orientalis and Quercus castanaefulia had fewer measurement errors than Parrotia persica, Alnus glutinosa and Carpinus betulus. Overall, the diameter RMSE at a height of 1 m and 1.30 m was 1.01 cm and 0.87 cm, respectively. According to the Bias index, the values measured with the TOF technology were higher than those measured with the caliper for most species were. Due to the results obtained and the many possibilities of TOF technology, such as the possibility to measure the diameter at different heights, the creation of RGB-3D, the high scanning speed and accuracy, low complexity, the brightness and the lower cost than other technologies, three-dimensional surveying is a good option for forest inventory verification.}, Keywords = {Forest inventory, RGB-D SLAM, Time of Flight, Point cloud, Caliper}, volume = {11}, Number = {4}, pages = {131-140}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-1081-en.html}, eprint = {http://jgst.issgeac.ir/article-1-1081-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2022} }