Original Research Paper
Remote Sensing
K. Ghobadi; E. Javadnia; H. Jalili; A. Zandkarimi
Abstract
Background and Objectives: Water vapor in the atmosphere is one of the most critical meteorological parameters, significantly influencing climate studies, weather forecasting, and climate change modeling. Precipitable Water Vapor (PWV) serves as a key indicator in atmospheric studies and is measured ...
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Background and Objectives: Water vapor in the atmosphere is one of the most critical meteorological parameters, significantly influencing climate studies, weather forecasting, and climate change modeling. Precipitable Water Vapor (PWV) serves as a key indicator in atmospheric studies and is measured using satellite data, including Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products. MODIS provides two PWV products: Near-Infrared (NIR), which is available only during the day, and Infrared (IR), which provides data for both day and night. Due to its broader temporal coverage, the IR product is widely utilized in various applications. However, the accuracy of this product, especially under varying atmospheric conditions during day and night, remains a major challenge. This study aims to enhance the accuracy of MODIS IR PWV data using machine learning and to assess the calibration's impact on day and night conditions.Methods: This study utilized data from 10 radiosonde stations in Iran during the 2019-2020 period as reference ground-truth data. Three datasets were prepared: raw MODIS data, fitted data, and modified data. A Multi-Layer Perceptron (MLP) model was employed for calibration and to evaluate its performance for day and night data separately. Standard machine learning methods were applied to design and implement the model. The model's accuracy was evaluated using the Root Mean Square Error (RMSE) and correlation coefficient (R) metrics. Findings: The results demonstrated that the MLP model significantly improved the accuracy of MODIS PWV data. During the day, RMSE decreased from 3.72 mm in the raw data to 2.63 mm in the calibrated model, while the correlation coefficient increased from 0.81 to 0.86. At night, RMSE reduced from 4.9 mm to 3.16 mm, and the correlation coefficient improved from 0.76 to 0.78. Overall, RMSE in raw MODIS data was 4.48 mm, which was reduced to 2.92 mm in the fitted model and 3.03 mm in the modified model. The correlation coefficient also improved from 0.77 to 0.87 and 0.85, respectively.Conclusion: This study confirmed that the MLP model effectively enhances the accuracy of MODIS PWV data and reduces existing errors under different atmospheric conditions. The primary innovation of this research is the application of the MLP model to calibrate satellite-derived PWV data for day and night conditions. By improving the precision of satellite data, this method enhances its reliability for practical applications, particularly in weather forecasting and climate studies. Limitations include dependency on radiosonde data as the reference and the absence of analysis on specific atmospheric factors influencing modeling. This approach can also be
Original Research Paper
Remote Sensing
P. Heidari; A. Milan; A.R. Gharagozlou
Abstract
Background and Objectives: Nowadays, getting land cover and land use information is crucial due to the growing number of uses for this data. The primary method for obtaining this information is considered to be through the utilization of remote sensing images. Image classification techniques should be ...
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Background and Objectives: Nowadays, getting land cover and land use information is crucial due to the growing number of uses for this data. The primary method for obtaining this information is considered to be through the utilization of remote sensing images. Image classification techniques should be employed so as to extract land cover and use from these images. Deep learning techniques can be utilized effectively to the classification of land cover and land use simply because of their great potential in image classification. But there are also challenges when applying these techniques as well. Model overfitting is one of the most common issues when utilizing deep learning algorithms. Another major issue with these methods is that they demand a significant amount of data during the training stage. Additionally, gradient exploding/vanishing and determining the suitable architecture are further challenges associated with these methods for extracting land cover and use from remote sensing imagery.Methods: The main objective of this research is to employ different techniques to overcome the challenges to achieve high classification accuracy. To solve the problem of model overfitting, dropout and early stopping approaches were utilized to ensure that the accuracy of the training and test data were close. The data augmentation strategy can prevent model overfitting in addition to addressing the lack of training data. As a result, this method was employed to augment training data and also avoiding model overfitting. The gradient clipping strategy was additionally used in this study to mitigate gradient exploding and vanishings in deep learning models. This study used the ResNet18 model to classify the EuroSat dataset, enabling us to obtain highly effective classification accuracy.Findings: Initially, this architecture was used with with the early stopping strategy, and the model had an overall accuracy of 91.19 percent and a kappa coefficient of 0.9018. The data augmentation technique was then applied to the same model, and the model achieved an overall accuracy of 91.78 percent with a kappa coefficient of 0.9085, surpassing the previous stage. In the last stage, a dropout method with a rate of 0.5 and a gradient clipping with a threshold of 0.1 were added to the previous model, and the model achieved an overall accuracy of 93.11 percent and a kappa coefficient of 0.9233, which was more accurate than the previous two stages.Conclusion: These results indicate that the EuroSat's land cover and land use classification accuracy in the final stage was higher than in prior stages.
Original Research Paper
Photogrammetry
M. Sharifzadeh; A.R. Gharagozlo; S. Sadeghian
Abstract
Background and Objectives: Archaeological artifacts, as the cultural and historical heritage of any society, play a crucial role in preserving its identity and history. Protecting and managing these artifacts requires modern technologies to accurately and non-destructively document and preserve this ...
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Background and Objectives: Archaeological artifacts, as the cultural and historical heritage of any society, play a crucial role in preserving its identity and history. Protecting and managing these artifacts requires modern technologies to accurately and non-destructively document and preserve this heritage. The use of photogrammetry and Geographic Information Systems (GIS) in managing and preserving historical sites, including archaeological areas, has increasingly gained attention. The primary aim of this research is to utilize photogrammetric technologies and GIS for the management and preservation of the ancient site of Hegmataneh in Hamadan. This research seeks to improve the accuracy and quality of data through aerial image processing, data correction, georeferencing, and precise map creation. Additionally, the research analyzes changes in the site and provides results to support planning and managerial decision-making in the field of historical site preservation. Furthermore, the goal is to analyze site changes and provide tools for planning and managerial decision-making in the preservation of historical sites.Methods: The ancient site of Hegmataneh in Hamadan was selected as the research site and sample due to its significant historical and cultural importance from the Median and Achaemenid periods. Data was collected using 12 aerial images with a resolution of 1 to 2 meters, captured with an Ultracam-Xp camera in 2017, along with Google Earth software for measuring and determining reference points.The materials used included image data and image processing software such as Agisoft Metashape Professional and Global Mapper to generate a Digital Elevation Model (DEM) and orthophoto. The research process involved preparing and processing images, correcting data, modeling, and creating maps. These steps progressively improved data accuracy and generated more precise 3D models for analyzing site changes.This research has primarily contributed to the management and preservation of archaeological sites and serves as a valuable decision-making tool for effective site management and preservation.Findings: This research utilized photogrammetry technologies and Geographic Information Systems (GIS) to create a spatial model of the Hegmataneh archaeological site in Hamadan. By processing aerial and satellite images, accurate 3D models were developed, enabling the analysis of structural and urban changes, as well as the creation of heritage maps for Hamadan and the Mapping Organization. The results demonstrated that combining these technologies helps in more accurate data recording, identifying threats, and supporting management planning. These methods enhance the accuracy of modeling and provide an efficient tool for decision-making in the preservation and management of cultural heritage.Conclusion: The restoration and proper conservation of historical monuments should be based on documented evidence and in compliance with national and international standards to prevent damage to the authenticity of the structures. Additionally, the design and construction of new buildings should be harmonized with historical structures and carried out under strict supervision. The use of modern technologies, such as aerial images, satellite imagery, artificial intelligence, and augmented reality, can improve the management and protection of heritage sites. The researcher’s suggestions include the need for precise documentation and continuous monitoring throughout all stages of restoration, design, and transfer of artifacts. There are limitations and challenges, such as a lack of scientific documentation, budgetary issues, and managerial obstacles, which must be carefully considered to prevent the destruction and damage of historical sites.
Original Research Paper
Remote Sensing
A. Sabzali Yameqani; A. A. Alesheikh
Abstract
Background and Objectives: Precise agricultural yield prediction is among the most important tools for managing agricultural resources, improving food security, and enhancing the productivity of international trade in agricultural products. The satellite remote sensing images has become widely adopted ...
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Background and Objectives: Precise agricultural yield prediction is among the most important tools for managing agricultural resources, improving food security, and enhancing the productivity of international trade in agricultural products. The satellite remote sensing images has become widely adopted because traditional methods cannot provide the needed accurate and timely predictions, and it covers large areas while providing accurate data. The advances in machine learning and ensemble learning have identified the complex interaction of environmental variables with crop yield. In modern times, ensemble learning models have achieved much higher prediction accuracy and provided useful insights to farmers and policy makers.This study aims to develop an innovative model that combines the XGBoost algorithm with the Pelican Optimization Algorithm (POA) to predict corn yields more accurately in the U.S. Midwest. The approach will provide an opportunity for the pre-harvest yield prediction by considering the plant phenological stages and optimal time range from July to August. The model will help the decision-makers to take effective measures on resource management to overcome the climate fluctuations and develop better agricultural policies.Methods and Materials: This research focuses on predicting corn yields in five key corn-producing states in the U.S. Midwest (Illinois, Iowa, Minnesota, North Dakota, and South Dakota). This paper will utilize remote sensing information, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), GPP (Gross Primary Production), and ET (Evapotranspiration); meteorological data, including temperature and precipitation; cropland data; and yield statistics during the growing season over the period 2011–2020 (May to September). XGBoost ensemble learning was used, whose hyperparameters were optimized with the Pelican Optimization Algorithm (POA) to enhance its accuracy. Filtration was performed on data using the VFI index. Nine years were used as training data, while one year was used as a test. For evaluating the performance, MAPE, MBE, MAE, RMSE, and the correlation coefficient have been used.Findings: The evaluation results of the POA-XGBoost model demonstrated its outstanding performance in predicting corn yields. During the 2011–2020 timeframe, validation trends highlighted variations in prediction accuracy and bias. In the first period, which includes 2011–2014, the errors went down and the prediction accuracy improved: MAPE reached 6.26%, while in 2014 the correlation coefficient increased to 0.9372. During the middle period of 2015–2018, the errors and positive bias showed an upward trend, especially during 2018, where MBE rose to 0.8039 and the correlation coefficient fell to 0.8083. However, the last two years (2019–2020) revealed much improved results: MAPE comprises 6.57%, while the correlation coefficient is as high as 0.9237 in 2020.Conclusion: The optimized POA-XGBoost model demonstrated high capability in predicting corn yields under diverse climatic conditions and can be extended to forecast other crops in the future. Advanced ensemble learning techniques combined with diverse data sources, such as satellite imagery and meteorological data, provide effective solutions for improving crop yield predictions. The study calls for the development of new hybrid models that will enable farmers and managers to better manage resources, increase productivity, and minimize risks.
Original Research Paper
Geo-spatial Information System
M.A. Momtazi; S. Sadeghian; A.R. Vafaeinejad
Abstract
Background and Objectives: As the value of real estate and land increases, people try to define the boundaries of their property more accurately in order to prevent the smallest losses. In order to increase the accuracy of real estate boundaries, one cannot be satisfied with flat positions, and on the ...
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Background and Objectives: As the value of real estate and land increases, people try to define the boundaries of their property more accurately in order to prevent the smallest losses. In order to increase the accuracy of real estate boundaries, one cannot be satisfied with flat positions, and on the other hand, new cadastral systems in urban management and meeting the needs of different users require the need to record the changes in urban spaces and use timely 3D models. Today, in order to provide a model for real estate and land management in cities, the third dimension, i.e. height, and the fourth dimension, i.e. time, should also be considered.Methods A combination of two methods is as follows: based on the purpose, in an applied manner, based on the results of previous research in this field, and based on the method of data collection in an experimental manner with a scientometric approachFindings: 3D modeling in cadastre should be created in the context of time to consider all aspects of land management in a country based on the passage of time. Currently, this time-based three-dimensional modeling is known as four-dimensional cadastre. In general, the applications of this research include the correct use of land and cadastre management, as well as the use of 3D cadastre height and 4D cadastre time, construction growth from different aspects, and the need to implement cadastre in the third dimension (elevation ) and the fourth (time) exists, the increasing complexity and flexibility of modern use requires that cadastres must manage information related to the third and time (fourth) dimensions In order to design a four-dimensional cadastre, it is very necessary and important to pay attention to things such as cost, time and available equipment. Land preparation, defining property boundaries, developing infrastructure facilities such as water and sewage networks, electricity and gas, managing urban green spaces and changing the use of these areas are also used in cultural heritage and reconstruction of old buildings. Preventing the phenomenon of land grabbing, along with the forgery of documents or theft and illegal possessions, is also one of the other uses.Conclusion: The conclusion based on the studies done in this field is as follows This study proposes a general framework for how to apply temporal information to the model, which is created by integrating cadastral information with LADM on legal objects and CityGML on physical objects. The main requirements for creating a 4D cadastral model for Iran with legal and physical views of existing cadastral objects are presented. This study was tested using real data in a case study. Therefore, this modeling of cadastral data, and management and service delivery steps, have been investigated separately in the scope of the research and are similar to manuals created for different users. The difference between our study and other studies in this field is that all the processing steps (analysis, modeling, storage, transformation and service of data, visualization) performed for 3D cadastral studies are explained and from the software Open source is used in scope.
Original Research Paper
Remote Sensing
A. Bayat; M. Seif; B. Asghari Beirami
Abstract
Background and Objectives: Landslides, as one of the most destructive natural phenomena, pose a serious threat to engineering and environmental structures. The Siah Bisheh Pumped Storage Dam, the first concrete-faced pumped storage dam in Iran, is at risk of geological displacements due to its geographical ...
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Background and Objectives: Landslides, as one of the most destructive natural phenomena, pose a serious threat to engineering and environmental structures. The Siah Bisheh Pumped Storage Dam, the first concrete-faced pumped storage dam in Iran, is at risk of geological displacements due to its geographical location in the Alborz Mountains and complex geological conditions (including active faults, unstable rock masses, and steep slopes). This study aimed to monitor land surface changes and manage landslide hazards within the dam area, using ground surveying and interferometric radar (InSAR) methods. The main focus was on analyzing the impact of key factors such as rapid drawdown, water level fluctuations, and construction activities on the instability of the area.Methods: In this study, two main methods were used. In the first method, called Ground surveying, changes in critical points such as Gully 5 and areas adjacent to Chalus Road have been monitored by installing concrete targets (small benchmarks) and periodically measuring planar and elevation displacements since 2011. In the second method, changes in the land surface were examined with millimeter accuracy using Sentinel-1 satellite data from 2015 to 2022 and image processing in SNAP software. The processing steps included image alignment, removal of topographic effects with a digital elevation model, Goldstein phase filtering, and phase-to-displacement conversion.Findings: The results showed significant agreement between the two methods. The highest displacements occurred in areas close to dam basins (such as Gully 5 with more than 60 cm of elevation displacement) and areas adjacent to Chalus Road. Rapid reservoir subsidence was identified as the main cause of instability, as sudden changes in water level accelerate the saturation and rapid depletion of soil and rock masses. The InSAR method was confirmed as a low-cost and faster tool compared to traditional land surveying methods, with displacement estimates of 0.13 to 0.5 meters in identifying changes over large areas.Conclusion: The combination of ground and satellite methods allows for more comprehensive and continuous monitoring of unstable areas. It is suggested that radar interferometry be used for periodic monitoring and crisis management in similar projects. Also, creating a satellite database and integrating the results with dam behavior models will help reduce landslide risk. This study emphasizes the role of modern technologies in increasing the accuracy and reducing the costs of monitoring sensitive structures.
Original Research Paper
Photogrammetry
A. M. Moazezi Mehr-e-Tehran
Abstract
Background and Objectives: The first step in cultural heritage documentation is the preparation of accurate maps of the current state of the artifacts. Worldwide scientific advances have made it possible for heritage conservation to abandon traditional collection methods and use modern tools and methods. ...
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Background and Objectives: The first step in cultural heritage documentation is the preparation of accurate maps of the current state of the artifacts. Worldwide scientific advances have made it possible for heritage conservation to abandon traditional collection methods and use modern tools and methods. Utilizing them significantly reduces the documentation time and increases the accuracy and quality of this process. Among these methods that have received significant attention worldwide in recent years is photogrammetry. In Iran, photogrammetry has been considered in published articles for the past four decades in heritage documentation. However, the development process and attention in this area have not been studied. The brief and initial survey also does not show serious and extensive attention to this new technology in heritage and documentation. Accordingly, the present article seeks to draw an image of the status of related articles. Achieving this goal will make it possible to verify the assumption of the insignificance of research attention to the subject of heritage photogrammetry, and by systematically formulating the structural and content characteristics of these studies, it will be possible to identify research gaps in this field and guide future studies.Methods: The present study is a secondary study, and its philosophical paradigm is interpretive. The research approach is qualitative, and the data collection method is text-based. Accordingly, 56 articles were selected from the 1990s (when the first relevant article was published in Iran) to the second half of 2024. Based on the systematic review method and qualitative meta-analysis, these articles were analyzed from the perspective of their structural and content characteristics.Findings: The study shows that, in terms of content, Articles shifted from focusing on introducing the method and explaining the importance of digital documentation in the early years, today has on its agenda the development of a process of utilizing the photogrammetric method in heritage documentation to improve the accuracy and validity of information. On the other hand, although the method applied to account for a more significant share of the scale of architectural buildings and their related elements, attention to digital documentation of architectural complexes, ancient sites, and urban and rural settlements has also increased. From a structural perspective, a significant increase in articles is noticeable, especially in the last decade (84 percent of all articles). Similarly, civil engineering specialists have made the main contribution to the production of these studies (up to 56 percent of articles). Of course, in recent years, the participation of restoration, architectural, and archaeological experts in producing these articles has increased, which shows a desire and need for more serious attention to this topic among this group of experts.Conclusion: However, scientific Articles in Iran on the subject of heritage documentation with new technologies such as photogrammetry are not impressive in terms of quantity or quality. The publication of the majority of these studies in national conferences (up to 68%) and the inconsequential contribution of restoration experts in writing these Articles also indicate that the subject is growing in cultural heritage and conservation and has no scientific reliability. While considering the unique benefits of this method, including no need for physical contact with historical structures in the harvesting process, the speed of data mining, and access to the expected results of documentation, which can be archived, updated, and post-processed, it is necessary to direct research trends to the important and less-attention subject of digital documentation of heritage with the help of methods such as photogrammetry, to provide the possibility of optimal and principled protection of our country's cultural heritage and its transmission to future generations.
Original Research Paper
Photogrammetry
M. Ahmadi Asoor; A.R. Afary; E. Ghanbari parmehr
Abstract
Background and Objectives: Using UAV photogrammetry for corridor mapping in areas with significant differences between their longitudinal and lateral dimensions reduces the geometric strength of the imaging network and introduces numerous challenges. These challenges in UAV photogrammetry lead to increased ...
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Background and Objectives: Using UAV photogrammetry for corridor mapping in areas with significant differences between their longitudinal and lateral dimensions reduces the geometric strength of the imaging network and introduces numerous challenges. These challenges in UAV photogrammetry lead to increased project time and costs, as well as higher errors in triangulation calculations. Several parameters—such as the distribution and number of ground control points, camera calibration methods, the number of flight strips, and the use of precise camera positions obtained from the Global Navigation Satellite System (GNSS)—affect triangulation accuracy. This study examines the impact of these parameters on triangulation accuracy in corridor areas.Methods: The evaluation of parameters affecting triangulation accuracy was conducted on two corridor areas with lengths of two and a half and five kilometers. In this research, three parameters—distribution and number of ground control points, camera calibration methods, and photogrammetry software—were examined on a single flight strip, and multiple flight strips were used solely to evaluate the impact of the number of flight strips on triangulation accuracy. The results were assessed based on the root mean square errors of planimetric and altimetric errors at the check points. In this study, the photogrammetry software Pix4D and Agisoft Metashape were used for triangulation calculations.Findings: The evaluation results regarding the impact of the distribution and number of control points showed that using eight control points—distributed as follows: two points at the beginning, two points at the end, two in the middle of the corridor (all opposite each other across the width), and two additional points in between—can improve triangulation accuracy compared to other configurations. Additionally, using the self-calibration method for the camera in triangulation calculations yields more accurate results than using pre-calibrated cameras. Moreover, employing more than one flight strip increases the accuracy of triangulation results.Conclusion: By appropriately designing ground control points throughout the entire corridor and applying the self-calibration method for camera calibration—without relying on precise image center coordinates—it is possible to achieve an accuracy of less than one meter for producing engineering maps at scales of 1:500, 1:1000, and 1:2000, with contour intervals of 0.5 meters, 1 meter, and 2 meters, respectively.
Original Research Paper
Geo-spatial Information System
N. Akhlaghi; A. Milan; A.R. Vafaeinejad
Abstract
Background and Objectives: In recent years, with the development of urbanization and the construction of apartments and complex buildings, the importance and necessity of 3D data, especially in the real estate information sector, has been given more attention. Building Information Modeling is a new technology ...
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Background and Objectives: In recent years, with the development of urbanization and the construction of apartments and complex buildings, the importance and necessity of 3D data, especially in the real estate information sector, has been given more attention. Building Information Modeling is a new technology in the construction industry that is used to create, manage and share a digital model of a building, its components and related information. In addition to increasing interoperability and collecting all building information and documents in a single model, by adapting the building information model and the structure whose construction has been completed, it can be used for demarcation purposes to specify the common areas, the boundaries and the extent of ownership of each person from the various components of the building and for property documentation, for accurate property pricing.Methods: In this research, first, the parameters affecting the valuation of the property will be identified and the required data will be collected according to the parameters. Then, using two-dimensional architectural and structural drawings drawn in CAD (which can be obtained from engineering or engineering systems offices), the building information model has been reconstructed. After that, for each of the building components, legal enrichment (specifying common areas and building amenities) has been performed and the value of the materials used in the building has been defined based on the price list for the building components. The proposed process in this research has been modeled in three dimensions in Revit software.Findings: The building information model is used throughout the entire life cycle of a building, from design to construction, operation, and even maintenance, but it does not actually have the ability to model ownership relations and legal information; however, with the method implemented in this study, due to the importance of clarifying ownership relations in land or buildings, especially in court cases, ownership and legal relations can be defined; in addition to defining legal relations, the modeling carried out for a sample of a constructed building has been carried out for accurate valuation according to the location and effective parameters.Conclusion: Finally, the share of each owner of the building components is determined by defining the joint and several relationships. In addition to accurately and unambiguously recording the amount of ownership of individuals in the building, this method can also be used to accurately price the property considering effective external and internal factors and reduce legal claims. Pricing is done using a price list for all building components and materials, which can be used to obtain the total price.
Original Research Paper
Geo-spatial Information System
M. Noormohamadi; Gh.r. Fallahi
Abstract
Background and Objectives: The significant increase in the global population during the 20th century and the beginning of the 21st century has had profound impacts on land-use planning and development. With the rapid population growth, rising from approximately 1.6 billion people at the start of the ...
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Background and Objectives: The significant increase in the global population during the 20th century and the beginning of the 21st century has had profound impacts on land-use planning and development. With the rapid population growth, rising from approximately 1.6 billion people at the start of the 20th century to over 7.5 billion in the early years of the 21st century, cities and urban areas have faced immense pressures. The primary goal of LAS (Land Administration Systems) is to support land markets and facilitate land management in both developed and developing countries. These systems play a vital role in securing ownership, protecting property rights, and facilitating land transactions, which in turn have a significant impact on economic growth and sustainable development. Cadastre is one of the main tools in land administration systems. Given the rapid growth of cities and the complexities arising from land use in urban environments, land information management has become more critical than ever before. In this study, the focus is on modeling underground features. The primary objective of this research is to explore different methods for modeling underground features and the use of appropriate standards for visualizing these features in a 3D cadastre. This research examines various data models and storage formats and ultimately aims to compare and select the best method for visualizing and displaying underground data in urban environments.Methods: For the modeling of underground features, a needs assessment and the development of a conceptual model were first undertaken, followed by an examination of data models and data storage formats. Two main data models for modeling underground features in a 3D cadastre were considered: the IFC model and the CityGML model. In the context of 3D urban environment modeling, two important standards were introduced: IFC and CityGML. In this study, CityGML was selected as the appropriate standard for visualizing underground features. Finally, three CityGML encoding methods, including XML/GML, 3DCityDB, and CityJSON, were reviewed and compared. Among them, CityJSON was chosen as the preferred encoding method for data visualization.Findings: The results of this study show that the development of a 3D model of urban wastewater infrastructure has clarified the relationship between underground infrastructure and land ownership. This modeling, in addition to identifying and visualizing the infrastructure, helps prevent potential damage and identifies weaknesses and vulnerabilities before problems occur. Furthermore, using this model enables analysis and urban planning for the future. The use of CityJSON, due to its simpler structure compared to XML, not only facilitates working with data but also reduces data volume and increases processing speed, making it a suitable tool for web developers and WebGIS projects.Conclusion: The implementation of this approach in developing cities, especially in cities with complex infrastructures like Tehran, could revolutionize urban management and planning. Using 3D modeling of underground features and displaying them in the CityJSON format, compared to traditional 2D cadastral methods, provides new capabilities for land and underground resource management, aiding in decision-making for urban management.
Original Research Paper
Geo-spatial Information System
A. Zamiri; A. A. Alesheikh; B. Atazadeh; J. Jafari
Abstract
Background and Objectives: Land administration plays a pivotal role in sustainable development, urban planning, and protecting property rights. In Iran, land information and cadastral systems face challenges such as data fragmentation, the involvement of multiple responsible organizations, and a lack ...
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Background and Objectives: Land administration plays a pivotal role in sustainable development, urban planning, and protecting property rights. In Iran, land information and cadastral systems face challenges such as data fragmentation, the involvement of multiple responsible organizations, and a lack of standardization. The Land Administration Domain Model (LADM), as an international standard (ISO 19152), provides an integrated framework for modeling legal, spatial, and administrative aspects of land. Given the specific characteristics of the registration system and the governing laws of land administration in Iran, it is essential to localize this model according to national needs. LADM allows for the development of country profiles tailored to the legal and institutional structures of each nation. This domain model has been implemented in more than 40 countries worldwide, including the Netherlands, Malaysia, Poland, Brazil, Morocco, Australia, and South Korea. These countries have taken advantage of the model’s flexibility to design modern land registration and three‑dimensional cadastre systems, reduce costs, and accelerate registration processes. The aim of this study is to develop a country profile of LADM for Iran that, by considering the legal, institutional, and technical structures of the country, provides a comprehensive conceptual model for land administration.Methods: This research begins with an international comparative analysis of cadastral frameworks, followed by a systematic evaluation of Iran's land registration system. Methodologically, the national profile formulation was organized into three distinct phases. First, the scope was defined, including the identification of stakeholders, legal and technical requirements. Then, the profile was designed by mapping LADM classes to Iran’s cadastral elements, creating new classes and attributes tailored to national needs, developing code lists, and designing the Unified Modelling Language (UML) model. Finally, the model was evaluated using ISO 19152 conformance tests and expert surveys. Data collection tools included analysis of registration documents, expert interviews, and review of international models. The analyses were conducted based on conformance assessment at three levels: low, medium and high.Findings: The analysis results showed that the proposed Iran country profile achieved full conformance with Level two (medium level) of the LADM standard, and several Level three (high level) components were also detected. Key accomplishments of this model include the successful mapping between classes, the addition of specialized classes and new attributes, and the redefinition of existing code lists. Alongside these technical outcomes, expert surveys likewise indicated the model’s strong acceptance and its effectiveness in improving land registration processes, reducing ownership conflicts, and enhancing the efficiency of the national registration system.Conclusion: The LADM country profile represents a fundamental step toward standardizing and advancing the national land registration system. By enabling three‑dimensional cadastre implementation and integration with the Building Information Model (BIM), it can serve as a powerful tool for cadastre management. Experiences from other countries also confirm that using LADM increases transparency, reduces registration costs, and enhances coordination among executing agencies. However, achieving full compliance with the standard and operational utilization requires technical revisions, infrastructure optimization, and sustained collaboration among the involved institutions. This study recommends that the developed model be implemented in real‑world environments and that its performance be evaluated under Iran’s operational conditions. Furthermore, future research could explore the integration of this model with other information systems and further development of the country profile.
Original Research Paper
Geo-spatial Information System
N. Bahrami; M. Argany; A. D. Boloorani; A.r. Vafaeinejad
Abstract
Background and Objectives: Earthquakes, among the most unpredictable and devastating natural disasters, result in significant human casualties and financial losses worldwide each year. Their sudden occurrence and destructive potential categorize them as critical crises that demand efficient and innovative ...
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Background and Objectives: Earthquakes, among the most unpredictable and devastating natural disasters, result in significant human casualties and financial losses worldwide each year. Their sudden occurrence and destructive potential categorize them as critical crises that demand efficient and innovative management strategies. Contemporary crisis management practices emphasize three key phases: preparedness (before the event), response (during the event), and recovery (after the event). Among these, rescue operations, which are part of the response phase, play a vital role in saving lives and mitigating further damage. However, given the urgency and complexity of rescue efforts, enhancing their effectiveness through innovative methods is essential. This study introduces a novel approach that leverages spatial intelligence—specifically Geo-Artificial Intelligence (Geo-AI)—to optimize rescue operations in the aftermath of an earthquake.Methods: This research proposes a Geo-AI–based framework to enhance rescue performance following an earthquake. The approach involves simulating a hypothetical earthquake scenario in Tehran using the Japan International Cooperation Agency (JICA) floating scenario model. A total of 48 rescuers are organized into six teams within the designated study area. These teams are tasked with conducting search and rescue missions facilitated by an augmented intelligent spatial information system. Unlike traditional or manually assigned rescue operations, the proposed model employs reinforcement learning—a subfield of artificial intelligence—to dynamically allocate resources and optimize operational decisions in real-time. The design incorporates a comprehensive set of variables known to influence post-earthquake rescue effectiveness, including team location, response time, victim survivability, and route accessibility. The aim is to minimize response times and maximize the number of successful rescues using spatially informed decision-making.Findings: Due to the inherent unpredictability of earthquakes and the logistical constraints of studying rescue operations in real-world post-disaster settings, this research relies on simulation to replicate realistic conditions. The simulation environment provides detailed spatial and descriptive information regarding both the affected area and the status of rescue teams. Furthermore, it enables estimation of structural and human damage resulting from the hypothetical earthquake. Based on these simulated conditions, rescue operations are prioritized according to urgency and resource availability. All 48 rescuers are initially positioned at the nearest crisis management center and are subsequently deployed based on the optimized task allocation strategy generated by the Geo-AI model. The simulation results show that using the proposed model significantly improves the allocation efficiency of rescue teams.Conclusion: The Geo-AI–driven rescue model presented in this study offers a promising new avenue for enhancing the quality and efficiency of post-earthquake search and rescue efforts. Simulation results demonstrate that variables such as survival time under rubble, task completion time, proximity of rescuers to affected sites, and travel speed are critical to the effectiveness of rescue missions. Implementation of the intelligent model led to a 2.642-fold improvement in task allocation efficiency compared to traditional methods. These findings highlight the transformative potential of integrating artificial intelligence and spatial data systems into disaster response frameworks.
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