@article{ 
author = {Alizadeh, Niloofar and Maghsoudi, Yasser and Managhebi, Tayabe},  
title = {Investigating the Possibility of Collapse Forewarning Identification in Urban Areas Based on Sentinel-1 InSAR Time Series}, 
abstract ={Sudden collapse, especially in urban areas, in addition to financial damage due to the destruction and threat of infrastructures and the occurrence of problems in urban management, and more importantly, the threat to human lives is considered in the series of high-risk urban accidents. Obviously, detecting the possibility of an accident before the accident will have a significant impact on reducing the consequences and effects of the collapse. In this article, the capability of radar remote sensing in detecting collapse forewarning in urban area has been evaluated. For this purpose, a 15-month time series of Sentinel-1 radar images was used to investigate the occurrence of collapse in the two areas of Abshanasan Boulevard and Karimkhan Street located in Tehran. The studied accidents occurred in May 1401 and February 1400, respectively, and therefore the time series from 15 months before the accident until the time of the collapse were analysed. In the first stage, subsidence rate maps of both regions were produced using two time series techniques of radar interferometry based on the permanent scatterers and small baseline and subset technique. The experimental results confirm the high correlation of the 15-month subsidence velocity obtained from the two mentioned techniques in both study areas with a determination coefficient of 0.9. In the second stage, applying a spatio-temporal analysis on the set of 15-month time series curves within the radius of 50 meters of both collapse accident sites was considered. Examining the results of both interferometric techniques confirms the capability and superiority of the small baseline and subset technique compared to the permanent scatterers InSAR technique in identifying the predictor of the collapse event in the days leading up to the accident. The SBAS method successfully identified the high-risk point near both the Abshenasan Boulevard and Karimkhan Street incidents, at distances of 1 meter and 2.5 meters from the event location, respectively. In contrast, the method based on permanent scatterer points did not perform as well in this context.  },  
Keywords = {Collapse, Interferometry, Sentinel-1, Spatio-temporal analysis, Small baseline, Permanent scatterer},
volume = {14},
Number = {3}, 
pages = {1-14}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.1},
url = {http://jgst.issgeac.ir/article-1-1196-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1196-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Narges, Zahra and Jelokhani-Niaraki, Mohammadreza and Mahmoody-Vanolya, Narjes and Hamzeh, Saei},  
title = {Monitoring pests and diseases of Fig orchards using the Public Participation Geographic Information System (Case study: Estahban city in Fars province)}, 
abstract ={Every year, fig orchards are affected by a variety of pests and plant diseases. Various weather and environmental parameters, as well as gardeners' activities during the year, might influence the spread of these pests. Because of their experience and knowledge of the present conditions in the area, gardeners can directly participate in the monitoring and identification of pests and diseases in fig orchards. To participate of gardeners in Estahban city in this research, a Public Participation Geographic Information System (PPGIS) has been developed and used in the area. This system allows gardeners to report and record pest-affected garden areas as well as share how to conduct gardening activities from 2022 to 2023. All gardeners will be able to learn about the conditions and kinds of pests present in the area by entering this data into the system. The following evaluates and analyzes the relationship between the many pests and diseases kinds that have been linked to various environmental and weather parameters as well as the methods used to carry out various gardening activities. The results of using a PPGIS demonstrated that the most common pests and diseases reported are fig mite pest and Bargzardo (yellow leaves) disease in 2022 and fig fly pest and fig souring disease in 2023. These results are attributed to the yearly variations in weather parameters and gardener activities. The system's usability evaluation also revealed that 85% of the gardeners required previous education to use it, and over half of them (65%) emphasized the system's compatibility, ease of use, and potential for improvement across its many components.},  
Keywords = {Pest Monitoring, Public Participation Geographic Information System, PPGIS, Fig orchards, Estahban},
volume = {14},
Number = {3}, 
pages = {15-28}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.15},
url = {http://jgst.issgeac.ir/article-1-1190-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1190-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Tavakoli, Mina and Abdi, Naser and Hasani, Hadiseh sadat},  
title = {Determination of the coordinates of image centers in UAV photogrammetry based on PPP}, 
abstract ={Nowadays, UAV photogrammetry has been applied in wide range of applications, due to its ability in high quality data acquisition in short time and achieving high accuracy in 3D modelling. In recent years, Global Navigation Satellite Systems (GNSS) has been considered in UAV photogrammetry for the purpose of perspective center determination. They can generate the production of photogrammetry with higher quality and also reduce number of ground control points. Although, relative positioning methods have been successfully applied in UAV photogrammetry, requiring the base station is the main challenge of them. In this study, the efficiency of Precise Point Positioning (PPP) has been evaluated in perspective center determination. It can eliminate the base station and be as accurate as relative methods by formulating errors. For this purpose, UAV dataset with kinematic GNSS receiver observation from Qatour city in West Azarbaijan is used. In the proposed method, perspective centers are determined based on online processing service NRCan and aerial triangulation performed by determined positions. In order to compare the obtained results with standard method, the process is also performed based on post-processed kinematics (PPK) and REDtoolbox software. The obtained results show that UAV photogrammetry based on PPP achieves high accuracy if UAV receiver observation, image acquisition time, the distance between perspective center and phase center of antenna and global coordinate systems transformation parameters are available. Comparison of PPK and PPP shows mean difference of 2 and 7 centimeters which proves the ability of PPP method. Moreover, the coordinates of control and check points are computed based on PPP and PPK. The accuracy of PPP is 10.6 and 12.3 and PPK is 10.5 and 11.6 centimeter. Consequently, numerical analysis proves that PPP can be appropriate alternative for PPK and RTK in UAV photogrammetry with preserving accuracy and quality and removing base reference.},  
Keywords = {GCP, GNSS, PPK, PPP, UAV photogrammetry},
volume = {14},
Number = {3}, 
pages = {29-42}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.29},
url = {http://jgst.issgeac.ir/article-1-1187-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1187-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Malek, Mohammad and Habibian, Mahdi and Karimi, Mohamm},  
title = {Recommending Spatio-Temporal Sequence in indoor space based on social network analysis (case study: scientific conference)}, 
abstract ={Every year, various events such as exhibitions and conferences are held all over the world. An event consists of various actors, the most important of which are the participants and the items in that event. Participants need to get the most out of these events at the right time. Therefore, providing a sequence of space-time recommendations for an event such as a scientific conference is a very significant issue. The scientific conference environment is a dynamic space where different items are held in different places and times. On the other hand, the location of the user in the conference space is constantly changing. Therefore, recommendations to users should be made according to the location of the user and the time of the request. To find a solution for this problem, focusing on the case study of scientific conferences, we used the ability of recommender systems and social network analysis. The characteristics of the users were extracted from their pages on the social-scientific network ResearchGate and the Google Scholar website. The suggested recommendation method is a combination of social filtering, content-based filtering, and Spatio-temporal filtering methods. The extracted social information from the sources is analyzed by social filtering methods. As a result, similar participants to the user are recognized. The content information of these recognized participants is used to improve recommendations and prevent over-specialization. In addition, social filtering analyzes the relationship among the participations to solve the cold start problem and by recognizing expert participation, it uses their content information to recommend to new users. Content information of the user and similar participation to this user inter the content-based filtering for each user. In this filtering, the similarity value of items with the content information of the user is calculated for each time window. For this purpose, using the spatial graph model, the conference space becomes a computing space. Space-time refinement by using this computing space and taking into account the location and time of the user's request and the items, suggests the most optimal item to the user and follows it by providing the space-time suggestions to the user. The performance of the system in providing suggestions to users was evaluated by a questionnaire. According to this survey, the accuracy of the recommendations made using the proposed method was evaluated at 74%, and the prioritization ability at 93%. Also, in the survey conducted, the ability to recognize experts for a new user was investigated. As a result, 76% of new users confirmed the authenticity of both introduced experts.  },  
Keywords = {Recommendation of Spatio-Temporal Sequence, Social Networks, Spatio-Temporal Filtering, Recommendation in Events},
volume = {14},
Number = {3}, 
pages = {43-57}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.43},
url = {http://jgst.issgeac.ir/article-1-1181-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1181-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Amini, Jalal and YounesiSinaki, Ali},  
title = {Designing and construction a ground-based soil moisture sensor in order to provide ground truth data for remote sensing images}, 
abstract ={Soil moisture is a highly significant variable in hydrological issues, playing a crucial role in hydrological modeling, quantitative meteorological forecasting, and the analysis of issues such as drought, forest fires, climate change, and water resource management. In recent years, remote sensing has gained significant attention for soil moisture estimation due to its speed, regular coverage, extensive reach, and cost-effectiveness. However, one of the primary concerns for remote sensing specialists in soil moisture estimation is the provision of ground-truth data. ‏In this research, the design and development of a ground-based soil moisture sensor were undertaken, which is capable of measuring soil moisture, collecting spatial data, and displaying and storing this information to provide ground-truth soil moisture data. Using this sensor, along with remote sensing imagery and artificial intelligence (AI) methods, it is possible to estimate soil moisture across a vast area. This process involves the simultaneous use of the sensor for sampling a small area during the satellite pass over the targeted region, which is then used to train the selected AI model. The trained model subsequently estimates soil moisture over the desired area using remote sensing imagery. ‏Factors such as soil texture, soil electrolytes, and temperature impact the sensor’s measurements. Therefore, evaluating these influencing factors and ensuring appropriate environmental conditions during laboratory testing and sensor calibration are critically important. The laboratory process will reveal the specific conditions and extent to which these factors affect the sensor’s output. In the sensor calibration process, a third-degree polynomial regression model was developed to determine the soil’s gravimetric moisture content, achieving an accuracy of 0.95% for gravimetric soil moisture content and a coefficient of determination of 97%.},  
Keywords = {moisture meter, sensitivity analysis , regression model, artificial intelligence},
volume = {14},
Number = {3}, 
pages = {59-68}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.59},
url = {http://jgst.issgeac.ir/article-1-1159-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1159-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Rezaali, Ali and Ebadi, Hamid and Farhadi, Hadi},  
title = {Spatiotemporal Monitoring of Saline Water Body Changes Using Remote Sensing Data with a Focus on Comparing Spectral Indices (Case Study: Lake Urmia)}, 
abstract ={The water crisis is one of the primary threats and challenges facing Iran and the world. A decrease in precipitation, along with inadequate water resource management, has led to the crisis of Lake Urmia, the largest salt lake in Iran. The lake's shrinking area has resulted in environmental problems and salt storms, necessitating continuous monitoring. Various methodologies have been developed for monitoring water bodies through remote sensing, each with advantages and disadvantages. Therefore, the present study focuses on the monitoring, evaluation, and comparison of the performance of spectral indices—AWEISh, AWEInSh, NDWI, and NDVI—in delineating water bodies using Landsat-8 satellite imagery in Google Earth Engine. This assessment was conducted seasonally from 2018 to 2024 and employed the automatic Edge Otsu thresholding algorithm. The results indicate that NDWI achieved the highest accuracy, with an overall accuracy of 99% and a Kappa coefficient of 0.97. In contrast, AWEISh achieved the lowest accuracy, with an overall accuracy of 78% and a Kappa coefficient of 0.57. Additionally, both visual and statistical analyses demonstrated that the AWEISh and AWEInSh indices provided low accuracy in distinguishing the water class from the salt class. Also, monitoring the water surface area revealed that the rate of change from 2018 to 2024 followed a declining pattern, with this decrease being more pronounced in the last two years. As a result, the average surface area of Lake Urmia during the 2018 to 2024 period was 2493.15, 3312.99, 3265.43, 2824.86, 2332.02 and 1959.32 km2, respectively, showing annual changes of 32.88%, -1.43%, -13.49%, -17.44%, and -15.98%. Consequently, among the spectral indices studied, the NDWI index exhibited the best performance in monitoring Lake Urmia, with the results indicating a significant reduction in the lake's water surface area in recent years.},  
Keywords = {Remote Sensing, Thresholding, Water Body, Spectral Index, Water Resources Monitoring, Lake Urmia.},
volume = {14},
Number = {3}, 
pages = {69-87}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.69},
url = {http://jgst.issgeac.ir/article-1-1201-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1201-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Shafiei, Mehran and Milan, Asghar},  
title = {Examining the degree of stability of the internal parameters camera of smartphones in the videogrammetric method to calculate the volume of earthworks}, 
abstract ={Nowadays, with the advancement of technology, the enhancement of computers computational power, and the availability of user-friendly software, there is a trend towards replacing inexpensive tools such as smartphones with expensive and specialized equipment for 3D modeling increased. Therefore, these tools must be evaluated based on scientific criteria. For this reason, in this study, the capabilities of smartphone cameras as data collection tools have been examined. To examine the stability of internal orientation parameters, firstly the cameras of ten smartphones were calibrated three times over fourteen days using a chessboard pattern and Vertical lines. The results showed continuous changes in their internal parameter values. To investigate whether the instability is caused by the weakness of the calibration method, the imaging geometry, or the structure of the smartphone camera, the most stable item was selected and re-calibrated twice in a row, and then its results were used. The proposed process was implemented on two concrete samples with a regular shape and one sand sample with an irregular shape on a small scale, whose volumes were precisely determined in advance by laboratory methods.  The volume of the samples was estimated with the videogrammetry method and implementation of pre-calibration and self-calibration. The effect of pre-calibration and self-calibration methods on the accuracy of volume estimation in the laboratory scale was very insignificant. The results showed differences from a minimum of 1.28% to a maximum of 2.98% with the actual volumes. In the final investigation, the study was conducted on a workshop-scale sand depot with approximate dimensions of 3.50Hx11Wx16L meters. The number of twenty ground points around the feature was defined using coded targets and the coordinates of their were taken by Total-station. To verify the accuracy of the measurements, a target rod with a known length was obliquely placed on the sand depot. The volume of the sand depot was also determined by surveying using a Total station, and it was used as the basis for comparing the accuracy of the videogrammetry method. The measurement error of the target rod length on the two models produced in the pre-calibration and self-calibration modes was determined to be 8 and 3 mm, respectively. In both modes, models with uniform distribution and high density of points were obtained, which had a difference of 5.8% and 1% relative to the volume, respectively estimated by the Total-station method.  },  
Keywords = {Self-calibration, smartphone, Structure from Motion, pre-calibration, 3D model},
volume = {14},
Number = {3}, 
pages = {89-113}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.89},
url = {http://jgst.issgeac.ir/article-1-1192-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1192-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}

@article{ 
author = {Farhangi, Mahdi and Aghabalaei, Amir and Jamour, Yahy},  
title = {A Review on Performance Optimization of Remote Sensing CubeSats Using Artificial Intelligence Techniques: Attitude Control, Data Transmission, and Thermal Management}, 
abstract ={Remote sensing CubeSats are valuable tools in space missions. This paper analyses the key challenges and innovations in three essential domains of these satellites: Attitude Determination and Control Systems (ADCS), data transmission to Earth, and thermal management. In the ADCS section, the paper examines challenges related to size and weight limitations, high precision, and the need for rapid responsiveness. The use of artificial intelligence and machine learning algorithms to enhance the performance of these systems—including neural networks and extended Kalman filters for CubeSat attitude control—is explored, with positive impacts on accuracy and error reduction analyzed. The data transmission section reviews issues related to bandwidth, data volume management, and transmission delays. The optimization of the data transmission process through compression algorithms and artificial intelligence techniques for data management and classification is discussed, focusing on reducing unnecessary data and improving communication efficiency. The thermal management section analyses temperature control challenges in CubeSats and proposes solutions such as thermal materials and coatings, along with precise thermal behavior simulation using artificial neural networks. This study evaluates methods for optimizing and predicting thermal conditions to maintain the performance of sensitive systems in space environments, with results indicating reduced processing time and computational costs. The paper also reviews innovative projects such as IPEX and Amazonia-1, analyzing the impact of AI technologies on enhancing CubeSat performance and efficiency. The findings of this review could contribute to advancing space technologies and enhancing remote sensing mission capabilities.},  
Keywords = {CubeSat, Remote Sensing, Artificial Intelligence, Performance Optimizations, Attitude Control, Thermal Management},
volume = {14},
Number = {3}, 
pages = {115-125}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},

doi = {10.61186/jgst.14.3.115},
url = {http://jgst.issgeac.ir/article-1-1207-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-1207-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2025}  
}