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In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas. [ABSTRACT FROM AUTHOR]

Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods. [ABSTRACT FROM AUTHOR]

Accurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited. [ABSTRACT FROM AUTHOR]

Digital Elevation Models (DEMs) are widely utilized in a variety of fields, including, hydrology, geomorphology, and geoscience. The dataset is now openly and freely available to the public, with access permissions for data at 30- and 90-meter resolution having been expanded. The Copernicus DEM is a Digital Surface Model (DSM) that depicts the Earth's surface, which includes structures, infrastructure, and vegetation. This DEM is based on the WorldDEM DSM, which has been altered. The Copernicus DEM's vertical accuracy is assessed in this paper. The study area is in Iran, north of Tehran and west of Jam. The Copernicus DEM is compared to the Shuttle Radar Topography Mission (SRTM) in terms of vertical accuracy. The results of the experiments reveal that the Copernicus DEM is more accurate than the SRTM. In the Tehran and Jam examples, the root mean square error (RMSE) for Copernicus DEM (30 m) is 5.72 and 2.19 meters, respectively. Also, this value for SRTM is 6.10 and 3.95 respectively. [ABSTRACT FROM AUTHOR]

Shopping centers are among the major economic and commercial places in cities in terms of social function, urban sustainability, environment, etc. Therefore, it is crucial to determine suitable locations for building new shopping centers. In this regard, urban vacant lands can be considered as high-potential locations to transform the urban landscape and enhance socio-economic development. The main purpose of this study is to assess the potential of urban vacant lands based on a spatial multi-criteria decision-making (SMCDM) system for building shopping centers in 22 districts of Tehran. In this study, first, 16 effective spatial criteria for locating the new shopping centers were identified using expert opinions and a literature review. The best-worst method (BWM) and the Min/Max method were used to calculate the weight and standardized values of each criterion. Then, the ordered weighted averaging (OWA) method was used to prepare a potential map of urban vacant lands for the construction of a shopping center under different decision-making scenarios, including very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. Finally, the spatial distribution of potential locations in the 22 districts of Tehran was investigated. The results showed that among the different criteria, the distance from the highway networks and distance from public transportation stations had the most weight, whereas the distance from fault and distance from stream networks had the least weight. The number of vacant lands with a very high potential for building a shopping center in Tehran based on very pessimistic, pessimistic, intermediate, optimistic and very optimistic scenarios was obtained at 29, 95, 105, 122, and 224, respectively. An increase in the degree of optimism in the attitude of decision-makers or investors increased the number of available options in the very high potential category. Under all scenarios, all vacant lands in 10 of Tehran's 22 districts with very high potential for building shopping centers. The results of the proposed model in this study can be useful and practical for a wide range of planners, decision-makers, and investors with different mental attitudes and risk tolerance. Close attention to these results can contribute to achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

The spatial models dealing with urban growth dynamics have been widely studied, while rare works have considered under-developed countries. Several problems have been detected in creating, calibrating and applying urban growth models and changing land use. The present work aims the modelling land-use changes through the CA-GA model in which a frame is provided for analysis and producing a map of growth patterns in urban areas in different spatial scales to study and analyse the increasing urban growth in Tehran. To consider the land-use changes in Tehran, ETM+TM images of 1985, 1992, 2000, and 2020 were selected to be analysed by the CA-GA algorithm to model the growth of the urban areas. The total kappa of results in Tehran is about 0.93, indicating the required precision and confidence of applied combinative genetic-Cellular automata modelling methods to model urban development. [ABSTRACT FROM AUTHOR]

This paper develops and applies algorithms for optimally merging satellite precipitation products with rain-gauge precipitation for accurate rainfall estimation. The satellite-based precipitation products (SBPs) PERSIANN-CDR, TMPA-3B42, GPM-IMERG, and GSMaP MKV are combined and evaluated to generate accurate rainfall estimates. Daily satellite precipitation data are compared with corresponding station data to calculate the bias for the period 2014–2019. Three different algorithms are proposed whose adjustable parameters are optimally determined by solving constrained optimization algorithms to produce combinations of satellite-based precipitation products. The optimal combination is named optimally merged satellite-based precipitation (OMSBP). The root mean square error (RMSE), coefficient correlation (CC), and the Nash–Sutcliffe error (NSE) are employed to test the proposed method with precipitation data for the Tehran urban region, Iran. The spatially resolved results over the studied urban area establish that TMPA-3B42, with an average value MAE, MBE, and RMSE equal to 0.68 mm, − 0.31 mm, and 2.94 mm, leads to better estimates of precipitation than those of PERSIANN-CDR, IMERG, and GSMaP MKV. Moreover, algorithms alg7 and alg8 yielded better results with respect to the MAE and MBE, respectively. Lastly, algorithm alg3 produced better results than alg7 and alg8 based on the RMSE, NSE, and CC corresponding to precipitation predictions. [ABSTRACT FROM AUTHOR]

The introduction of new techniques to improve the robustness and accuracy of vehicle detection is always important for the intelligent transportation system as it may face different problems and challenges. Conventional image-based vehicle detection methods have presented difficulties in acquiring good images due to perspective and background noise, poor lighting and weather conditions. We propose a high-accurate, vehicle detection method by using Modified Inverse Perspective Mapping. Thus, the perspective effect is removed, and then the Hough transform was applied to extract road lines and lanes. Gaussian Mixture Model and chromaticity-based strategy were applied to segment the moving vehicles and tackle shadow effects, respectively. We evaluated the performance of the proposed method under recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas). Results indicate that the proposed approach is feasible, and more accurate compared to others, especially when facing bad weather conditions and lighting variations in different environments. [ABSTRACT FROM AUTHOR]