@article{ author = {MoeiniRoudbali, Ali and AliAbbaspour, Rahim and Chehreghan, Alirez}, title = {Matching of Polygon Objects by Optimizing Geometric Criteria}, abstract ={Despite the semantic criteria, geometric criteria have different performances on polygon feature matching in different vector datasets. By using these criteria for measuring the similarity of two polygons in all matchings, the same results would not have been obtained. To achieve the best matching results, the determination of optimal geometric criteria for each dataset is considered necessary. In previous research, the most used geometric criteria are the overlap area between two features, the Euclidian distance between two features, the orientation difference of two features, and the shape similarity of two features. In addition to determining the impact factor of each criterion in the best result, the best geometric criteria combination should be specified. In this study, unlike previous studies which have considered object matching as a unique issue in all datasets, objects matching is considered as a separate issue in each dataset and by converting the problem as an optimization problem, an approach is proposed to define optimal weights of criteria for different datasets using a genetic algorithm. In each dataset, corresponding best weights have distinguished that lead to the best matching result. To evaluate the proposed approach, a variety of spatial datasets of residential buildings have been used including a part of Bandar Abbas city in 1:25000, 1:50000, and 1:100000 scales; a part of district 6 of Tehran city in 1:25000 and 1:50000 scales; and a part of Rasht city in 1:25000, 1:50000, and 1:100000 scales. The results showed that the proposed approach has done a good performance in both polygon feature matching and identifying six corresponding relationship classes in all study areas. Moreover, matching results have been improved by an average of 28.61% compared to the case where all criteria are considered with equal weights and an average of 9.13% compared to the case that criteria are assessed according to expert opinions.}, Keywords = {Polygon Feature Matching, Geometric Criteria, Optimization, Genetic Algorithm}, volume = {9}, Number = {3}, pages = {1-24}, publisher = {kntu}, doi = {10.52547/jgit.9.3.1}, url = {http://jgit.kntu.ac.ir/article-1-830-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-830-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} } @article{ author = {Omati, Mehrnoosh and Sahebi, Mhmod Reza and Aghababaei, Hossei}, title = {Determination of height of urban buildings based on non-parametric estimation of signal spectrum in SAR data tomography}, abstract ={Nowadays, the TomoSAR technique has been able to overcome the limitations of radar interferometry techniques in separating multiple scatterers of pixels. By extending the principles of virtual aperture in the elevation direction, these techniques pay much attention in the analysis of urban challenging areas. Despite the expectation of interference of the distribution of buildings with different heights, wall and ceiling levels, ground, or tree trunks, the TomoSAR technique enables the retrieval of reflectivity function along elevation direction by applying a stack of images taken at different times and slightly different orbit positions. In this research, the capability of a new non-parametric method of signal spectrum estimation in the analysis of the third dimension of buildings in urban areas is investigated. The proposed efficient maximum entropy estimator has been able to provide good performance in reconstructing three-dimensional information from the urban area. By searching for coefficients of the automatic regression model, this method can maximize the signal entropy, also separate the different noise levels. Implementation of the proposed algorithm using 19 TerraSAR-X satellite images in the study of the third-millennium tower in Tehran with a height of 120 meters and comparison of the results with capon and Beamforming spectral estimation methods, indicates better performance of the proposed method in height reconstruction, the continuity reflectivity profile and the elimination of the effects of the side lobes.}, Keywords = {Tomography Technique, Spectral Estimation Analysis, TerraSAR-X Images, Urban Area.}, volume = {9}, Number = {3}, pages = {25-38}, publisher = {kntu}, doi = {10.52547/jgit.9.3.25}, url = {http://jgit.kntu.ac.ir/article-1-751-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-751-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} } @article{ author = {Rajabi, Ahmad and Shahhoseini, Rez}, title = {Analysis of changes detection in Gano coal mine area using satellite image from 2000 to 2020 (northwest of Damghan)}, abstract ={In coal mines, fires and explosions due to rising temperatures and high coal densities are the most likely hazards. Due to the looseness of the coal-bearing terrestrial layers, there are also risks of collapsing extraction tunnels. Therefore, in order to manage the risk in coal mines, the risk model in these areas should be studied periodically. The purpose of this study is to comprehensively study the changes in the region in order to introduce parts of the mineral range that are endangered due to surface and altitude thermal changes. In the present study, identification of thermal changes in Gano coal mine from 2000 to 2020 using Landsat satellite data and surface elevation changes of the mine between 2014 to 2020 using Sentinel1 radar data in northwest Damghan is done. In this study, first the parts of the study area where the density of coal seams is high is determined using the normalized index of two Landsat data bands (SWIR1 and SWIR2 bands) and the experimental threshold of 0.06. Surface temperature (LST) was also estimated using the Planck relationship and Landsat data. LST threshold values ​​were estimated for both years to detect fire-hazardous pixels. Using radar interferometry and images of 2014 and 2020 Sentinel1 satellite of the region, digital elevation model and altitude changes of the mine area were extracted. The maximum subsidence was about 9 cm, which had fallen and caused casualties during the field visit.}, Keywords = {Coal, Fire Hazard, LST, Landsat, Sentinel1}, volume = {9}, Number = {3}, pages = {39-57}, publisher = {kntu}, doi = {10.52547/jgit.9.3.39}, url = {http://jgit.kntu.ac.ir/article-1-826-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-826-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} } @article{ author = {hashemi, vahid and mesgari, mohammad sadi and mohammadikazaj, pooy}, title = {Solving the ridesharing problem with Non-homogeneous vehicles by using an improved genetic algorithm and the social preferences of the users}, abstract ={Most existing ridesharing systems perform travel planning based only on two criteria of spatial and temporal similarity of travelers. In general, neglecting the social preferences caused to reduce users' willingness to use ridesharing services. To achieve this purpose a system should be designed and implemented not just based on two necessary conditions of spatial and temporal similarities, but also based on the similarities between users in terms of their social and personal preferences to plan a travel. This study aims to create and implement a suitable model for ridesharing systems by using vehicles with different capacities, using an advanced genetic algorithm, and considering users' social preferences to determine similar passengers. In this study, two innovative mutation operators and two local search algorithms have been applied to improve the genetic algorithm in this particular case. In this model, a mechanism has been designed to help users to share feedbacks based on their travel experience on a hypothetical social network with other users. In this way, users' opinions on each other to use or not to use this system and why it can be analyzed and examined. The results obtained from the analysis and evaluations of the implemented model indicate efficiency and success.}, Keywords = {Keywords: Social preferences of users, improved genetic algorithm, Innovative mutation operators, ridesharing feedback mechanism, Social network of users.}, volume = {9}, Number = {3}, pages = {59-84}, publisher = {kntu}, doi = {10.52547/jgit.9.3.59}, url = {http://jgit.kntu.ac.ir/article-1-758-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-758-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} } @article{ author = {Soleimani, Masoud and Attarchi, Sara and Mahmoody-Vanolya, Narjes and Bakhshizadeh, Farimah and Ahmadi, Hame}, title = {Evaluation of Sentinel-1 Interferometric SAR Coherence efficiency for Land Cover Mapping}, abstract ={In this study, the capabilities of Interferometric Synthetic Aperture Radar (InSAR) time series data and machine learning have been evaluated for land cover mapping in Iran. In this way, a time series of Sentinel-1 SAR data (including 16 SLC images with approximately 24 days time interval) from 2018 to 2020 were used for a region of Ahvaz County located in Khuzestan province. Using InSAR processing, 25 coherence images were obtained based on different SAR pairs. Five dominant land cover classes in the region including built-up lands, agricultural lands, water bodies, bare soil, and dense natural vegetation cover were identified and considered. Through Google Earth's high-resolution imagery, a total of 4,930 ground truth samples with appropriate spatial distribution were acquired for all classes. The obtained multi-temporal coherence images were used as input variables to the support vector machine (SVM) classifier. The training and validation process of different SVM kernels was performed using 80% and 20% of the ground truth samples, respectively. Overall accuracy in different kernels including linear, 2th-degree polynomial, 4th-degree polynomial, 6th-degree polynomial, radial base function (RBF), and sigmoid were computed 60.7, 64.7, 67.7, 69.9, 66.3, and 59.5%, respectively. Likewise, Kappa coefficients were reported 50. 8, 55.87, 59.62, 62.38, 57.87, and 49.38%, respectively. Accordingly, the highest and lowest overall accuracy and Kappa coefficient were belong to the 6th-degree polynomial and sigmoid kernels, respectively. Based on the user and producer accuracy assessments in all kernels, the built-up lands has the highest accuracy (93%–up to 98.5%), and in opposite the dense vegetation has the lowest accuracy (11%–up to 56.25%). Generally, the results emphasize the high potential of Sentinel-1 InSAR coherence data in land cover mapping. Meanwhile, the contribution of the classifier to the efficiency of data is also important.}, Keywords = {Land cover mapping, Classification, Support Vector Machine (SVM), Interferometric SAR (InSAR), Coherence}, volume = {9}, Number = {3}, pages = {85-107}, publisher = {kntu}, doi = {10.52547/jgit.9.3.85}, url = {http://jgit.kntu.ac.ir/article-1-819-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-819-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} } @article{ author = {Sheikhghaderi, Hedayat and Zeaieanfirouzabadi, Parviz and Kelarestaghi, Manoochehr}, title = {Evaluation and comparison performance of deep neural networks FCN and RDRCNN in order to identify and extract urban road using images of Sentinel-2 with medium spatial resolution}, abstract ={Road extraction using remote sensing images has been one of the most interesting topics for researchers in recent years. Recently, the development of deep neural networks (DNNs) in the field of semantic segmentation has become one of the important methods of Road extraction. In the Meanwhile The majority of research in the field of road extraction using DNN in urban and non-urban areas has been done using images with high spatial resolution. In this research, for the first time, to extract the road using DNN, the images with medium spatial resolution of Sentinel-2 sensor were used, so that the image of Tehran as a test data and from 7 other cities (Mashhad, Isfahan, Shiraz, Tabriz, Kermanshah, Urmia and Baghdad) were used as training and validation data. In the Meanwhile, after preparing and labeling all the pixels related to the road surface, the images are converted into 256 × 256 pieces, and after separating the unsuitable parts, for test, training and validation data, respectively. 135, 1500 and 100 image pieces were obtained. Finally, deep refined residual convolution neural networks (RDRCNN) and U-Net, which are based on fully convolutional networks (FCN), were used to train and extract the road complication. The results show that both RDRCNN and FCN models have well identified and extracted Tehran urban road network from Sentinel 2 images in comparison with the ground reality data. Meanwhile, the FCN model performed better than the RDRCNN model both visually and in terms of accuracy assessment metrics, so that for the FCN model, the criteria Recall 82.92%, accuracy 77.67%, F1 score 77.53 and overall accuracy 96. 30% and for RDRCNN the criteria Recall were 80.43%, accuracy 71.37, F1 score 72.14% and overall accuracy 95.71%. In general, the findings of this study show the potential of using DNN methods to extract urban roads using images with medium spatial resolution of Sentinel-2.}, Keywords = {deep neural networks(DNN), Road Extraction, RDRCNN, FCN, Sentinel-2}, volume = {9}, Number = {3}, pages = {109-133}, publisher = {kntu}, doi = {10.52547/jgit.9.3.109}, url = {http://jgit.kntu.ac.ir/article-1-840-en.html}, eprint = {http://jgit.kntu.ac.ir/article-1-840-en.pdf}, journal = {Engineering Journal of Geospatial Information Technology}, issn = {2008-9635}, eissn = {}, year = {2021} }