Original Research Paper
Geodesy
M. Shirazian; F. Haj Mahmoud Attar
Abstract
Background and Objectives: Vertical Skewness is a prevalent anomaly in the field of geodetic science, which arises due to the displacement of the vertical component on the geoid at various locations. This discrepancy directly impacts both horizontal and vertical angles that are observed, and ...
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Background and Objectives: Vertical Skewness is a prevalent anomaly in the field of geodetic science, which arises due to the displacement of the vertical component on the geoid at various locations. This discrepancy directly impacts both horizontal and vertical angles that are observed, and indirectly influences measurements of lengths. When considering the adjustment of length conversions to the horizon, this phenomenon is adequately represented by vertical angles. Consequently, vertical angles assume a significant role in ameliorating the effects of geoid updrafts and ensuring the precision of length determinations.The occurrence of refraction exerts a substantial influence on observations of angles. This impact, particularly on the vertical angle, possesses a considerable magnitude that gives rise to a substantial discrepancy when adjusting the transformation of lengths to the horizon. A prevalent approach employed to mitigate the influence of refraction involves the simultaneous measurement of vertical angles in both directions at two distinct endpoints of equivalent distances.Methods: There exist two primary categories of coordinate system commonly employed to express the positions of points in geodesy. These categories are known as the geocentric coordinate system, which centers on the Earth, and the topocentric coordinate system, which also centers on the Earth. In the geocentric coordinate system, the origin of the coordinates coincides with the Earth's center of gravity, and the z-axis is defined in alignment with the Earth's epoch axis. On the other hand, in the topocentric coordinate system, the origin of the coordinates corresponds to a specific point on the Earth's surface, namely the location of the camera. Furthermore, the z-axis in this coordinate system corresponds to the surface of the parallel potential passing over the aforementioned point where the camera is situated, also known as the line of work passing over the point.Geodetic measurements of both horizontal and vertical angles are conducted within topocentric coordinate systems. As indicated, the prevailing technique for mitigating the impact of refraction on vertical angles involves simultaneously reading said angles from both the initial and terminal positions along the lengths. Given that the starting and ending points of the lengths exhibit dissimilar vertical extensions on the potential surface, the measurement of the vertical angle, and consequently the correction of the length's conversion to the horizon, are subjected to a significant degree of error.Findings: The current investigation comprehensively examines this error and its consequential impacts on the horizontal spacing of points within small-scale geodesic networks. To achieve this objective, four specific regions in Sweden characterized by accurate geoids were meticulously chosen, and an elliptical procedure was implemented on the geoid of these regions to determine the parameters of the geoid surface. Furthermore, the geoid surface was computed.Conclusion: The findings of this investigation demonstrate that the significance of the skewness of geoid gauges is evident even in geodetic networks of small-scales, and should not be disregarded. It is important to consider that the assessment of the magnitude of the skewness effect of geoid perpendiculars is only feasible in areas where a precise geoid is present. Consequently, it becomes unfeasible to entirely eliminate this effect when observing vertical angles simultaneously in areas lacking accurate geoids. Consequently, an alternative approach must be employed to rectify the conversion to the mile-long horizon. Further examination of this alternative method is presented in subsequent sections of this scholarly article.
Original Research Paper
Remote Sensing
M. Khoshsima; S. ghazanfarinia; R. Narimani
Abstract
Background and Objectives: Remote sensing satellites equipped with Lidar payloads are deployed for ground, atmospheric, and space target monitoring missions. The primary advantage of space Lidars lies in their ability to conduct global monitoring with repeated coverage of targets, a capability that ground ...
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Background and Objectives: Remote sensing satellites equipped with Lidar payloads are deployed for ground, atmospheric, and space target monitoring missions. The primary advantage of space Lidars lies in their ability to conduct global monitoring with repeated coverage of targets, a capability that ground and airborne Lidars cannot fulfill. The energy of each pulse is a critical parameter in the design of space Lidar remote sensing payloads, impacting data accuracy, signal-to-noise ratio, horizontal and vertical resolution, and overall return signal aggregation time. Enhancing the signal-to-noise ratio is a key consideration in both the design and operational phases of a Lidar project. Numerous studies have explored the impact of different parameters on the signal-to-noise ratio of Lidar systems. However, despite these extensive investigations, this issue has not been comprehensively examined to date. The geometric configuration of the Lidar system, laser specifications, optics, electronics, and the arrangement of the laser-telescope geometry are key factors that significantly influence the optimization of the signal-to-noise ratio. Apart from the specifications of individual components, system analyses play a crucial role in the design of Lidar payloads. This includes technical specifications of the laser, transmitter, optical system, receiver telescope, heat control, and radiation considerations. Establishing a technical alignment between missions and payload specifications is a key requirement in this process.Methods: Fully investigating the challenges associated with the systematic analysis of Lidar payloads is essential. This paper presents comprehensive research on the challenges and requirements related to the design considerations of Lidar systems, focusing on the transmitter and receiver components, as well as environmental factors such as radiation effects and thermal issues. Following the initial system analysis, further exploration is needed to address considerations for the Lidar payload during the operational phase, encompassing challenges related to data extraction, signal quality, and signal-to-noise ratio.Findings: Variations in the sun's radiation angle can impact the optical depth parameter of aerosols, affecting the lidar signal-to-noise ratio by 10-40% based on atmospheric conditions. Optimal data collection times are estimated around zenith angles below 50 degrees at approximately 10 am and 2 pm, correlating with sun angle and atmospheric light scattering. Additionally, sunrise and sunset can influence signal-to-noise ratio due to maximum dispersion. The calculation of total ionizing dose damage serves as a design bottleneck, determining laser module efficiency loss through critical power index assessment for active and passive heat control. This article explores technical bottlenecks and systemic considerations in lidar payloads, investigating the role of environmental factors such as sun radiation angle and space environment impact.Conclusion: The findings indicate that environmental factors such as space radiation and atmospheric optical indices during the operational phase, as well as geometric, structural parameters, and heat management during the design phase, significantly impact the energy of each pulse and variations in the signal-to-noise ratio. This insight is crucial for accurately estimating design budgets at both system and subsystem levels. The outcomes of this study not only offer practical implications for case studies but also suggest potential enhancements through the exploration and inclusion of additional considerations, potentially at the subsystem or other payload component levels. Leveraging the results of this research can help guarantee the precision of Lidar performance in future phases.
Original Research Paper
Geo-spatial Information System
A. Vafaeinejad; F. Hemati; S. Haery
Abstract
Background and Objectives: Monitoring and analyzing variations in land surface temperature is essential for agriculture, biodiversity, human health, and water resources as it is a major indication of climate change. By analyzing and assessing these alterations, one can get a thorough understanding of ...
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Background and Objectives: Monitoring and analyzing variations in land surface temperature is essential for agriculture, biodiversity, human health, and water resources as it is a major indication of climate change. By analyzing and assessing these alterations, one can get a thorough understanding of the causes and effects of global warming and be better equipped to take preventative and remedial action against this rapidly spreading phenomena. Because remote sensing data may show the features of land phenomena and their spatial distribution at various scales, it is crucial to gather LST. Several factors need to be taken into account, including data distribution and spatial correlation analysis, associated geostatistical model investigation, model selection, and result validation. In light of the foregoing, the purpose of this research is to ascertain the trend of variations in LST in Ilam County and to propose a suitable mathematical model for the interpolation of meteorological station data in the area.Methods: The thermal band of the Landsat 7 satellite has proven to be an effective tool in this research to study changes in surface temperature in Ilam city. The data in this band, which is recorded as thermal radiation emitted from the register, allows the surface to be calculated with a reasonable degree of accuracy. One benefit of using satellite images is their large coverage and periodicity, which allows the study of changes in surface temperature in a region and over time. This comprehensive view allows the temperature changes in Ilam city to be well-analyzed and the factors that affect these changes are identified in various locations. To do this, a regular grid with 291 points was sampled from satellite photos. Next, experimental variogram points were created using the geostatistical method, and various spatial models, including Gaussian, exponential, circular, and spherical, were fitted to the sampled data. Ultimately, distinct surface temperature maps have been produced using the normal kriging interpolation method and each of these models. The correctness of each map has been determined using statistical markers like the coefficient of determination and root mean square error.Findings: The findings of the study demonstrate that the thermal band data from the Landsat 7 satellite exhibits a Gaussian geographical pattern, and this model provides a strong justification for the observed spatial variations in surface temperature. The findings demonstrate that the Gaussian spatial model fits the experimental variogram of surface temperature in the investigated region the best. This demonstrates that variations in surface temperature in this area are spatially autocorrelated, with the correlation between locations decreasing with increasing distance. High accuracy interpolated maps are produced by the traditional kriging approach using the Gaussian model. These maps' coefficient of determination was 0.94, indicating a good degree of agreement between the estimated and real surface temperature data.Conclusion: The integration of remote sensing data and geostatistical methods offers a powerful tool for examining spatial variations and interpolating environmental data, including land surface temperature changes. In this study, the pattern of LST changes in Ilam County was determined using satellite remote sensing data, and the Gaussian model was introduced as the optimal spatial model for interpolating LST at weather stations. Data analysis revealed that LST in the region has increased significantly over the past few decades.
Original Research Paper
Geography
A. Sahebgharani
Abstract
Background and Objectives: Population growth in human settlements leads to an increase in land use demand. Consequently, optimal urban land use planning is critical for planners and decision-makers. Given that land use allocation involves multiple objectives and a large set of data and variables, solving ...
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Background and Objectives: Population growth in human settlements leads to an increase in land use demand. Consequently, optimal urban land use planning is critical for planners and decision-makers. Given that land use allocation involves multiple objectives and a large set of data and variables, solving this problem requires developing decision support systems (DSSs) and applying meta-heuristic algorithms.In this paper, a DSS equipped with an optimization method (i.e., Ant Colony Optimization algorithm) is developed to solve the land use allocation problem. The study aims to design a graphical user interface (GUI) to facilitate the algorithm implementation process and apply it to a study area to assess how such a tool can help achieve the optimal land use layout. Additionally, the outputs of the ACO are compared with the results of two other meta-heuristics (i.e., the Genetic and the Simulated Annealing algorithms) to evaluate the performance of the designed DSS.Methods: To fulfill the research objective, first, the land use optimization problem is formulated, which includes the decision variable and how it is discretized, three objective functions (i.e., compatibility, compactness, and suitability maximization), two area controlling constraints, and the way of combining the objective functions. Second, the ACO algorithm customized with the land use allocation problem is presented. Third, the study area, the 7th municipal district of Isfahan divided into 334 allocation cells, is introduced, and the requirements such as parameters and weights for calculating and combining the objective functions are described based on the case study characteristics, related studies, and expert opinions. Fourth, a code is developed, and a GUI is designed in MATLAB programming to carry out the computational process, solve the equations, and handle the spatial data. Finally, the ACO parameters are tuned, and the code is applied to the study area within the depicted DSS framework. Alongside the ACO implementation, two other meta-heuristics (i.e., the genetic and simulated annealing algorithms) are executed to constitute a ground for the performance analysis.Findings: Outputs of the developed DSS illustrated the land use distribution within the 7th municipal district of Isfahan and the ACO’s convergence process. It showed that the cultural and sports land types were in the central part of the study area, and a major amount of the service land types was placed close to the green spaces. In addition, service types were located in the central and northern parts of the study area providing access for the residents to such necessary amenities.Conclusion: The results indicated that the ACO algorithm performed satisfactorily in the study area. In other words, the DSS, including this algorithm, demonstrated effective land management and planning performance. It also displayed benefits for users interested in applying different objectives and constraints. Besides, the ACO performed better in the study area than the other utilized methods. Although this article delivered a DSS along with optimization algorithms advantageous for resource management and spatial planning, incorporating land use levels (e.g., urban and neighborhood) and compatibility of the modeling context with more realistic conditions (e.g., cell area variation) are proposed for future research that are of limitations of this article.
Original Research Paper
Remote Sensing
M. HeidariGholanlo; R. Javanmard Alitappeh; E. Ghnabari Parmehr
Abstract
Background and Objectives: With the advancement of technology and the emergence of multifunctional satellites, a significant amount of real time information is now transmitted from the Earth's surface. These satellites are equipped with sensors that obtain critical information by sending signals at various ...
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Background and Objectives: With the advancement of technology and the emergence of multifunctional satellites, a significant amount of real time information is now transmitted from the Earth's surface. These satellites are equipped with sensors that obtain critical information by sending signals at various frequencies to the Earth's surface. The data received from these satellites are utilized in various scientific and military applications, including aviation, geographical studies, meteorology, agriculture, and other research fields. The agricultural sector and crop monitoring have especially benefited from remote sensing methods compared to traditional methods, becoming a primary tool for collecting environmental information for area monitoring applications, attracting researchers' attention. One such application is regional monitoring to examine agricultural products over cultivated areas. The use of remote sensing tools and satellite images is highly efficient due to their wide regional coverage. To automatically analyze these images, classify, and segment cultivated areas, machine learning methods are currently employed. Among these methods, deep learning offers superior performance and higher speed compared to other learning methods, such as manual or semi-automatic methods.Methods: This paper utilizes deep learning models suitable for segmenting agricultural areas. Generally, these models produce an output of equivalent dimensions for each input. Therefore, for working with satellite images, an improved U-Net model is proposed in this study. The proposed model is developed using Vision Transformers (ViT) in the model's bottleneck for classifying and segmenting four types of agricultural products: rice, wheat, canola, and corn. Compared to convolutional layers, ViT is more efficient in terms of conceptual and algorithmic implementation and requires less computational power. This model addresses the problems and weaknesses of the base U-Net model that arise with complex datasets, diverse in shape, size, and texture, enabling more accurate and reliable segmentation results. Additionally, the proposed improvements enhance the model's robustness and adaptability to various agricultural scenarios.Findings: In the numerous experiments conducted, the proposed method achieved an accuracy of 83.84 and Precision of 70.69%, providing a better classification of the five target products compared to other methods. The qualitative outputs also indicate better segmentation of the input images when applying the proposed method. Alongside the accuracy metric, other metrics such as focal loss, recall, precision, and MIoU were examined, with the proposed method reaching acceptable levels in most cases. Notably, as the target area was in Iran, data collection and labeling were also carried out in this research, providing a suitable dataset for training other modelsConclusion: This research presents an end-to-end model for learning features related to the segmentation of satellite images. The results indicate that the proposed method can be effectively used for segmenting satellite images received from Sentinel-2 for the target products, such as various crops. Therefore, the results obtained can play a crucial role in water consumption management, planting structure adjustment, loss estimation, and agricultural performance evaluation, providing significant insights for stakeholders. By utilizing these methods, it is possible to achieve improved efficiency and accuracy in agricultural management and optimize resource use in this area, contributing to sustainable agricultural practices and better decision-making processes.
Review Paper
Remote Sensing
S. Barzegar; M. khoshsima
Abstract
Background and Objectives: Radio sounding and tomography techniques play a crucial role in studying the structure and dynamics of the ionosphere. Specifically, tomography is an advanced method for creating three-dimensional models of electron density within the ionospheric layer. By utilizing observational ...
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Background and Objectives: Radio sounding and tomography techniques play a crucial role in studying the structure and dynamics of the ionosphere. Specifically, tomography is an advanced method for creating three-dimensional models of electron density within the ionospheric layer. By utilizing observational data, such as GPS measurements, tomography generates accurate maps of electron distribution. Ionospheric tomography provides high-precision insights into temporal and spatial variations in electron density. This precision is essential for applications like satellite navigation, radio communications, and meteorological predictions. Researchers focus on the upper layers of Earth’s atmosphere, using specialized radars called ionosondes to obtain precise information about electron density and the structure of ionized layers. Tomography, an imaging technique, relies on radio wave propagation through the ionosphere. It produces two- or three-dimensional images of electron distribution within this layer. Widely used in weather forecasting, radio communications, and space studies, tomography significantly advances our understanding of ionospheric phenomena. Technological advancements, including satellite-based measurements, enable even more accurate analyses, ultimately enhancing global communication and aviation safety. In this paper, the existing method for how to obtain the electron density change of the ionosphere layer based on the total electron content (TEC) parameter by using the phase difference analysis created in the communication signal of the global navigation satellite system GNSS when passing through different layers of the ionosphere has been investigated and studied. For this purpose, communication signals from low-orbit and high-orbit satellites were studied, and the method of obtaining TEC from phase difference was explained for each. Then, we studied the existing methods and algorithms for converting TEC (Total Electron Content) data into tomographic images. At the end of this article, as an example, we implemented the radio tomography method to visualize plasma bubbles in the equatorial region and compared the results with images taken from optical instruments. It was shown that radio tomography can be used as an accurate method for visualizing the structure of plasma bubbles. At the end of this article, we compared the method studied here with methods such as all-sky imaging, incoherent scatter radars, etc., and discussed the advantages and disadvantages of these methods relative to each other.Methods: In current research on ionospheric sounding and tomography, significant progress has been made using the Global Navigation Satellite System (GNSS). Recent studies indicate that GNSS can model the ionospheric structure in three dimensions with high precision. Electron distribution in the ionosphere is analyzed using radio data obtained from satellites at Low Earth Orbit (LO) and High Earth Orbit (HO). Collecting ionospheric information via GNSS is a complex and precise process that relies on advanced technology to measure and analyze various ionospheric parameters. These systems, which include Earth-orbiting satellites, transmit signals to ground-based receiver stations. These signals contain precise temporal and spatial information about the satellites, allowing accurate determination of receiver positions on Earth. The distribution of electron density in the ionospheric layer directly affects the propagation of GNSS radio waves, including their path, shape, and phase. Any disruption in the ionospheric layer significantly impacts satellite communications, precise navigation, and long-range communications. In fact, GNSS utilizes this capability to measure the Total Electron Content (TEC) of the ionosphere, a key indicator for understanding its state. This process occurs through signals transmitted from satellites to ground stations. As these signals pass through the ionosphere, they are influenced by electron density variations, which can be measured with high accuracy.Findings: In this comprehensive study, current research on ionospheric radio tomography using Total Electron Content (TEC) measurements from GNSS has been conducted. The concept of TEC and its impact on the phase and shape of signals received from the examined satellites has been explored. The application and methodology of using Low Earth Orbit (LEO) and High Earth Orbit (HEO) satellite data to obtain detailed TEC information are described. The validation and accuracy assessment of satellite data in ionospheric radio tomography, which is crucial for the reliability of the final product and the production process, have been addressed. Finally, a technique for reconstructing tomographic images using TEC measurements via GNSS signals is reviewed. It has been demonstrated that this reconstruction technique works well for imaging plasma bubbles. Horizontal distributions obtained from Vertical TEC (VTEC) depletions are compared with images captured by optical instruments, yielding similar results. Even in regions where GNSS signals are weak, this method can yield good outcomes if the bubble structures are sufficiently large.Conclusion: In summary, GNSS tomography represents a dynamic and evolving field with significant potential for improving accuracy and efficiency in weather predictions. As we continue our research and development efforts, we anticipate the emergence of new methods and technologies that can address existing challenges and enhance the quality and precision of tomographic models. These advancements hold promise for diverse applications of GNSS tomography, including meteorology, climate change studies, and disaster management.
Original Research Paper
Remote Sensing
D. Akbari; M. Akbari
Abstract
target detection. The most common method for target detection in satellite images is pixel-based detection, in which each pixel is assigned to a specific class with only its spectral information and without considering neighboring pixels. With recent advances and the creation of images with high spatial ...
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target detection. The most common method for target detection in satellite images is pixel-based detection, in which each pixel is assigned to a specific class with only its spectral information and without considering neighboring pixels. With recent advances and the creation of images with high spatial resolution, it is necessary to use both spectral and spatial information to detect hyperspectral images. This research deals with the detection of roofs with special coverage as a target, in an urban environment, through a series of hyperspectral images. Since an urban environment has complex characteristics in terms of physics, geometry and elements used in buildings, hyperspectral data effectively helps to identify, extract and produce a map of the elements that make up an urban environment. Identifying the type of roof of buildings in urban environments is very important in various applications, such as mobile phone communications, virtual reality, architecture and urban modeling, planning, and city management.Methods: In this research, the spatial information strategy is investigated along with the spectral information to improve target detection in the analysis of hyperspectral images. For this purpose, the spectral-spatial algorithm of marker-based minimum spanning forest, which is used in the image classification process, is used to detect the roofs of buildings with special coverage. The markers were selected from the support vector machine classification map in the proposed method. For this purpose, the analysis of the labeling of the connected components was done based on 8 neighboring pixels. The minimum spanning forest is obtained after creating the minimum spanning tree and removing the ridges related to the added vertex in the last step. In the minimum spanning forest algorithm, each tree grows on one of the vertices of the image, and by assigning the class of each marker to all the pixels grown from it, a spectral-spatial detection map is obtained.Findings: The above techniques were applied on a series of CASI sensor image data taken from the urban area of Toulouse located in the south of France. The results of quantitative and qualitative evaluations show that the proposed method has improved the value of the Kappa coefficient by 38% in comparison with the spectral angle measurement detection algorithm. This shows the importance of using spatial information in the detection process, while the spectral angle measurement algorithm only needs the spectral information of the desired target for detection.Conclusion: Simultaneously with the growth of urbanization and the development of urban areas, the need of managers and planners for very accurate maps of urban areas has increased significantly. The use of spatial information, especially in the case of images taken from urban areas where several adjacent pixels belong to the same class or complex, can improve detection accuracy. It is intended to reduce the amount of error in the spectral-spatial detection of the target in the future research. The conditions of creating mixed pixels, such as the overlap of terrestrial phenomena and the heterogeneity of most phenomena, and as a result, the increase of the internal variance of the target, increase the detection error in hyperspectral images. Therefore, it is tried to reduce the above errors by using different methods.
Original Research Paper
Photogrammetry
M. Farhangi; A. Milan; S. Sadeghian
Abstract
Background and Objectives: Accurate land use classification is essential for effective natural resource management, urban planning, precision agriculture, and environmental monitoring. Such classification helps predict and prevent environmental issues. Methods like high-resolution satellite and aerial ...
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Background and Objectives: Accurate land use classification is essential for effective natural resource management, urban planning, precision agriculture, and environmental monitoring. Such classification helps predict and prevent environmental issues. Methods like high-resolution satellite and aerial imagery, GIS, and deep learning techniques, including Convolutional Neural Networks (CNN) and the U-Net architecture, offer high precision in analyzing and classifying aerial images. The U-Net network, known for its unique structure, excels in defining land use boundaries. This study focuses on a region in Poland, using the U-Net model to enhance classification accuracy and efficiency through regularization techniques and the Adam optimizer.Methods: This research used high-resolution aerial images and a deep learning model based on the U-Net architecture to achieve precise land use classification. The approach aimed at improving classification accuracy across four land use categories. High-resolution aerial images were collected, corrected geometrically and radiometrically to create orthophotos. These images were labeled and cropped to 256x256 pixels, with data augmentation techniques such as rotation and flipping applied. The dataset was divided into training (75%), validation (25%), and testing (5% of the validation set). The U-Net model includes convolutional blocks with 3x3 kernels, normalization layers, and dropout layers, organized into encoding, decoding, and output layers. Hyperparameters included the Adam optimizer, a learning rate of 0.0001, and a batch size of 16. Model performance was evaluated using metrics like overall accuracy, kappa coefficient, and Jaccard score.Findings: The algorithm was tested on data from Poznań, Poland, utilizing high-resolution aerial images from 2021 with a 25 cm spatial resolution. The data, labeled by experts, covered four land use types: buildings, forests, roads, and water. Out of 769 labeled images, 576 were used for training (expanded to 2304 samples after augmentation), 183 for validation, and 10 for testing. The model, developed using Python and Keras on TensorFlow and trained in Google Colab, achieved high accuracy after 96 iterations, validated against expert-labeled maps. While the U-Net model performed well in general classification, it encountered challenges with rare classes like water. Data augmentation and more samples for such classes could improve accuracy. The training and validation accuracy reached 0.95 and 0.85, respectively, with validation errors stabilizing around 0.5. The U-Net model demonstrated significant improvements in accuracy, kappa coefficient, and Jaccard index compared to previous studies, underscoring the importance of high-quality data and precise parameter tuning.Conclusion: The study assessed the U-Net deep learning model for accurate land use classification using aerial images. Results indicate that the model effectively identified and differentiated between land use types with high precision. The U-Net structure achieved an overall accuracy of 92.47%, a Jaccard index of 54.45%, and a kappa coefficient of 79.59%. These results demonstrate the model’s strong capability in defining class boundaries. Future improvements could involve utilizing multispectral and hyperspectral images for more detailed information, combining U-Net with other networks like ANN, optimizing hyperparameters with advanced search methods, and employing transfer learning, especially with limited training data. Implementing these strategies could enhance accuracy and efficiency in land use classification, with broader applications in scientific and practical fields.
Original Research Paper
Photogrammetry
M. Heidarimozaffar; S. A. Hosseini
Abstract
Background and Objectives: In recent decades, geomatics science has made significant progress, and these advances are due to advanced measurement tools and innovative technologies in the field of geometric and spatial data acquisition. In this context, portable laser scanners and UAVs have been introduced ...
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Background and Objectives: In recent decades, geomatics science has made significant progress, and these advances are due to advanced measurement tools and innovative technologies in the field of geometric and spatial data acquisition. In this context, portable laser scanners and UAVs have been introduced as basic and efficient tools that are capable of accurately and quickly measuring various objects and environments, including urban spaces. These devices automatically record all the details of the urban space in the form of point clouds or images. To extract the geometric information of buildings from these details, it is necessary to use machine vision methods. To achieve accurate and reliable models of buildings, a sequence of post-processing operations is implemented when processing point cloud data. One of the most important stages of these processes is the segmentation of the point cloud. These steps transform point cloud data into more conceptual and analyzable information. One of the important issues in processing point cloud data is the ability to extract flat surfaces of building facades (walls). These flat surfaces are of special importance as basic components in modeling and analyzing the condition of buildings. Accuracy in the information related to these flat surfaces allows for a more accurate and complete distinction between different components of buildings. This is important in several applications including urban planning, construction management, and energy consumption analysis of buildings.Methods: In this article, the combination of MSAC and G-DBSCAN algorithms is used to extract flat surfaces from three-point cloud datasets (point cloud obtained from GeoSLAM ZEB-HORIZON laser scanner devices, point cloud obtained from Phantom 4 Pro drone imaging and hybrid point cloud) has been These two algorithms are executed sequentially. The area chosen for this purpose is the buildings of the Faculty of Engineering of Bu-Ali Sina University in Hamedan. Because this environment has features such as architectural diversity, the existence of flat facades, and different ways of placing walls in relation to each other with different dimensions.Findings: This research, with a comprehensive evaluation of three separate data sets, shows an average precision of more than 97%, which guarantees high accuracy in data extraction. In addition, the average recall has reached more than 94%, which covers most of the elements of the facade. The result of this evaluation is the F1 score with an average of 95%, which indicates progress in the field of accurate building data extraction and architectural modeling. However, the algorithm encountered challenges when facing the walls that were perpendicular to the laser scanner's movement path, which reduced the representation rate. Also, the SfM algorithm has difficulty in generating points on window panes, which caused some points related to the space inside the windows to be recognized as wall points. This issue shows that point cloud generation algorithms from images affect the results of this algorithm. On the contrary, the results of the combined data have been very promising, in such a way that these data converged faster than the other two data sets in the first step of the algorithm and had high performance in Precision and Recall.Conclusion: However, the findings show that the algorithm has generally shown an outstanding performance in extracting building facade information, especially with the use of diverse and varied data. These developments are promising and open new horizons in spatial data analysis and building modeling. This innovative approach can be used in various applications and help to develop modern and data-driven architectural models.
Original Research Paper
Geo-spatial Information System
P. Afzali-Kordmahalleh; S. Behzad
Abstract
Background and Objectives: Object recognition is a widely discussed topic across various disciplines. However, identifying tree species in forests remains challenging due to their similar appearances and behaviors. This study aims to address this issue by leveraging the temporal-spectral signature model ...
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Background and Objectives: Object recognition is a widely discussed topic across various disciplines. However, identifying tree species in forests remains challenging due to their similar appearances and behaviors. This study aims to address this issue by leveraging the temporal-spectral signature model in Google Earth Engine (GEE) to differentiate forest plant species. The innovation of this research lies in determining the temporal-spectral behavior of tree species by calculating the brightness of satellite image bands across different months of the year, creating a unique matrix for each species.Methods: The study utilized Landsat 8 and 9 satellite imagery from 2016 to 2022, focusing on a section of the southern forests of Gilan province. Initially, spectral behavior curves for vegetation, land, and water were plotted. By coding in GEE, the average brightness values for each band were calculated, producing combined bar and line graphs for the three categories. Temporal-spectral signatures for tree species were then developed using typology maps and field surveys, with 200 data points collected for oak, hornbeam, beech, alder, and Bergan needle species. A matrix of 84×1 was formed, representing the temporal-spectral signature for each species, using Bands 1 to 7 of Landsat 8 across 12 months. MATLAB was employed to visualize the generated matrices.Findings: The results revealed distinct brightness levels in specific bands and months for different species. For instance, in the first band during the second month, brightness values for oak, hornbeam, beech, Bergan needle, and alder were 0.38, 0.31, 0.27, 0.46, and 0.25, respectively. The highest brightness levels for most species occurred in the fifth band during the tenth month. Classification using the random forest method with both 7-band and 84-band inputs showed that the innovative temporal-spectral signature approach improved the Kappa coefficient to approximately 0.4. This unique signature enables the accurate identification and differentiation of tree species, supported by field observations.Conclusion: The study demonstrates that temporal-spectral signatures can effectively differentiate tree species in forests, facilitating improved classification and monitoring. This approach holds potential for broader application to other species, paving the way for advanced forest management and monitoring by organizations such as natural resources and environmental agencies. Future research should extend this method to additional species to further enhance forest classification systems.
Original Research Paper
Remote Sensing
A. Amini; Z. Azargoshayesh; P. Nouri
Abstract
Background and Objectives: Today, air pollution is considered as one of the most important problems of human societies. The expansion of urbanization, the development of cities, the increase in population, the increase in construction, the development of industrial activities and the increase in the ...
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Background and Objectives: Today, air pollution is considered as one of the most important problems of human societies. The expansion of urbanization, the development of cities, the increase in population, the increase in construction, the development of industrial activities and the increase in the consumption of fossil fuels, the lack of an efficient public transportation system, the low quality of fuel and the traffic density have caused a large amount of pollutants incompatible with natural mechanisms to be released into the air daily. Air pollution has harmful effects on the environment and human health. The distribution of gases and pollutants in different layers of the atmosphere is not equal. So that some pollutants such as carbon monoxide and sulfur dioxide have a very high concentration in the troposphere layer compared to other layers. Along with other factors that increase pollutants, the construction industry is also one of the major sources of environmental pollution, responsible for about 4% of the production of greenhouse gases, particles and more water pollution than any other industry. Today, due to the excessive growth of the population and the need for housing, it has required that tall buildings rise every day from all corners of the city. Now this issue causes the production of pollutants to increase in cities. Because the increase in population causes an increase in single-passenger cars and issues in this regard. The increase in population and the desire to seek profit through construction replaces the beautiful and natural landscapes of our country with urban areas every year. Population growth causes an increase in construction in an area, and this problem causes the physical development of urban areas over time. In this research, the aim is to investigate the impact of construction on air pollution in the troposphere and also how the concentration of pollutants changes in different seasons of the year.Methods The area studied in this research is District 22 of the municipality located in the northwest of Tehran, one of the new areas of Tehran, which was built in order to solve the lack of services in the western area of Tehran and to relocate part of the population living in the dilapidated tissues of central Tehran, and the amount of construction in this area is growing. has been selected and to investigate the construction changes in the study area, the maximum likelihood classification method was used using Sentinel-2 satellite images, and for monitoring carbon monoxide, sulfur dioxide, nitrogen dioxide, tropospheric ozone and aerosol pollutants, Sentinel 5 satellite images were used in the Google Earth Engine system.Findings: The findings include calculating the amount of construction in the years 2018 to 2022 and providing maps of changes in the concentration of carbon monoxide, sulfur dioxide, nitrogen dioxide, tropospheric ozone and aerosol pollutants per unit of time and space.Conclusion: The results of this research show that from 2018 to 2022, about 33.12 hectares were added to the construction areas of Tehran's 22nd district, and these construction changes did not affect all gases, and only carbon monoxide and carbon dioxide pollutants had a growing trend. Also, the concentration of mentioned pollutants was higher in winter than other seasons.
Original Research Paper
Remote Sensing
A. Gholamian; F. Tabib Mahmoudi
Abstract
Background and Objectives: Roads are known as vital and essential elements in the development of cities, because they play a very important role in communication and transportation and represent the extent of urban development and growth. In order to increase accuracy and efficiency in road detection ...
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Background and Objectives: Roads are known as vital and essential elements in the development of cities, because they play a very important role in communication and transportation and represent the extent of urban development and growth. In order to increase accuracy and efficiency in road detection and classification, researchers have designed and used automatic methods based on deep learning algorithms. These approaches, due to their superior capabilities in recognizing patterns and complex features of images, have effectively replaced traditional methods and have significantly improved the accuracy and speed of road detection.Methods: In this paper, an improved UNet3+ encoder-decoder model has been used for road detection from remote sensing images. In this proposed model, pyramid pooling and spatial and channel attention modules are used to improve road detection results. The spatial attention module is used in the proposed network architecture to improve the network's focus on important locations in feature maps. The channel attention module also allows the network to more focus on important information and perform better at tasks such as feature detection and classification. The pyramid pooling module is designed to receive multi-scale information. This module helps the network to understand different spatial scales by applying averaging at different levels and then resizing the averaged features to the size of the original feature map.Findings: The evaluation of the capabilities of the proposed network in detecting secondary roads in areas with less residential density and with soil and vegetation cover shows the superiority of this network over the original version of UNet3+. The improved network proposed in this paper was able to detect roads more accurately. This shows the power of the network in detecting roads in conditions where there is less environmental interference. Quantitative results obtained from this network show the fact that the use of spatial and channel attention modules and pyramid pooling module has been able to increase the accuracy, recall, F1 score and IOU measures by 6, 15.6, 8.3 and 17.4, respectively, compared to the original version of the UNet3+ network.Conclusion: The challenges raised in the automatic roads detection from remote sensing images, including the effect of shadows and obstruction of the road with buildings and vegetation cover, and the similarity of the secondary roads with the soil background can lead to a decrease in the accuracy of recognizing roads from remote sensing images. The use of improved UNet3+ encoder-decoder architecture capabilities in this research was able to reduce some of these challenges and increase the accuracy of the detection results of secondary roads in areas with soil and vegetation.
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