Ihsan Qadir¹, Usama Naeem², Ahmed Nouman³, Aamir Raza⁴, Jun Wu^1,*

Revue Internationale de Géomatique, Vol.34, pp. 831-853, 2025, DOI:10.32604/rig.2025.069020 - 10 November 2025

Abstract The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades, leading to substantial economic losses, infrastructure damage, and socio-environmental disruptions. This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019. By utilizing satellite imagery from Landsat 5, Landsat 8, and Sentinel-2, key flood events were analyzed through the application of water indices such as the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) to delineate flood extents. Historical… More >

Irfan Abbas, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 483-502, 2025, DOI:10.32604/cmc.2025.066578 - 29 August 2025

Abstract Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part… More >

Asaad Algarni¹, Aysha Naseer ², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1981-1998, 2025, DOI:10.32604/cmc.2025.064268 - 29 August 2025

Abstract Remote sensing plays a pivotal role in environmental monitoring, disaster relief, and urban planning, where accurate scene classification of aerial images is essential. However, conventional convolutional neural networks (CNNs) struggle with long-range dependencies and preserving high-resolution features, limiting their effectiveness in complex aerial image analysis. To address these challenges, we propose a Hybrid HRNet-Swin Transformer model that synergizes the strengths of HRNet-W48 for high-resolution segmentation and the Swin Transformer for global feature extraction. This hybrid architecture ensures robust multi-scale feature fusion, capturing fine-grained details and broader contextual relationships in aerial imagery. Our methodology begins with… More >

Almustafa Abd Elkader Ayek¹, Bilel Zerouali^2,*, Ankur Srivastava³, Mohannad Ali Loho^4,5, Nadjem Bailek^6,7, Celso Augusto Guimarães Santos^8,9

Revue Internationale de Géomatique, Vol.34, pp. 669-689, 2025, DOI:10.32604/rig.2025.067137 - 19 August 2025

Abstract This study investigates the spatial and temporal dynamics of key air pollutants—nitrogen dioxide (NO₂), carbon monoxide (CO), methane (CH₄), formaldehyde (HCHO), and the ultraviolet aerosol index (UVAI)—over the period 2019–2024. Utilizing high-resolution remote sensing data from the Sentinel-5 Precursor satellite and its TROPOspheric Monitoring Instrument (TROPOMI) processed via Google Earth Engine (GEE), pollutant concentrations were analyzed, with spatial visualizations produced using ArcGIS Pro. The results reveal that urban and industrial hotspots—notably in Damascus, Aleppo, Homs, and Hama—exhibit elevated NO₂ and CO levels, strongly correlated with population density, traffic, and industrial emissions. Temporal trends indicate significant pollutant fluctuations More > Graphic Abstract

Uzair Abbas¹, Zahid Maqbool¹, Muhammad Adnan Shahid^1,2,*, Muhammad Safdar^1,2, Saif Ullah Khan^1,3

Revue Internationale de Géomatique, Vol.34, pp. 655-668, 2025, DOI:10.32604/rig.2025.064279 - 11 August 2025

Abstract This study was conducted to devise an integrated methodology for retrieval of surface soil moisture (SSM) using Landsat 8 TIRS data. For this purpose, Landsat 8 images of 15 May 2021 (pre-monsoon) and 20 November 2021 (post-monsoon) were processed for retrieval of soil moisture index (SMI) based on land surface temperature (LST). Moreover, field-based SM in the laboratory was also determined and correlated with satellite-based SMI. A moderate correlation between field-based SM and satellite-based SMI with R² = 0.60 was obtained. Based on this relationship, SSM maps of Tehsil Faisalabad Saddar for the pre-and post-monsoon seasons… More >

Lachezar Filchev^1,*, Milen Chanev¹, Galin Petrov²

Revue Internationale de Géomatique, Vol.34, pp. 513-533, 2025, DOI:10.32604/rig.2025.064280 - 06 August 2025

Abstract This review article provides a comprehensive analysis of Earth Observation (EO) applications for soil health assessment in Europe and abroad. The study explores the effectiveness of EO in capturing contextual information about various soil properties and conditions, as well as its role in monitoring soil health over time. The authors examine the current state of operational, semi-operational, and developing EO products and services relevant to soil health indicators. These include vegetation cover, forest cover, soil organic carbon, soil structure, landscape heterogeneity, and the presence of soil pollutants, excess nutrients, and salts. The review identifies gaps… More > Graphic Abstract

E. M. B. M. Karunathilake^1,#, Thanh Tuan Thai^1,2,3,#, Sheikh Mansoor¹, Anh Tuan Le^3,4, Faheem Shehzad Baloch^1,5, Yong Suk Chung^1,*, Dong-Wook Kim^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2215-2228, 2025, DOI:10.32604/phyton.2025.067439 - 31 July 2025

Abstract Accurate and timely estimation of above-ground biomass is crucial for understanding crop growth dynamics, optimizing agricultural input management, and assessing productivity in sustainable farming practices. However, conventional biomass assessments are destructive and resource-intensive. In contrast, remote sensing techniques, particularly those utilizing low-altitude unmanned aerial vehicles, provide a non-destructive approach to collect imagery data on plant canopy features, including spectral reflectance and structural details at any stage of the crop life cycle. This study explores the potential visible-light-derived vegetative indices to improve biomass prediction during the flowering period of buckwheat (Fagopyrum tataricum). Red, green, and blue (RGB)… More >

Abdulwahab Alazeb¹, Muhammad Hanzla², Naif Al Mudawi^1,*, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4677-4697, 2025, DOI:10.32604/cmc.2025.065899 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms More >

Naif Al Mudawi^1,*, Muhammad Hanzla², Abdulwahab Alazeb¹, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4491-4509, 2025, DOI:10.32604/cmc.2025.065490 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow… More >

Fawad Salam Khan^1,*, Noman Hasany², Sheikh Kamran Abid³, Muhammad Khurram⁴, Jerome Gacu^5,6,7, Cris Edward Monjardin⁸, Kevin Lawrence de Jesus⁷

Revue Internationale de Géomatique, Vol.34, pp. 415-432, 2025, DOI:10.32604/rig.2025.067026 - 29 July 2025

Abstract Noise present in remote sensing data creates obstacles to proper land use and land cover (LULC) classification methods. The paper evaluates machine learning (ML) denoising methods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover (LULC) mapping. A basic Raman spectroscopy model demonstrates that Savitzky-Golay (SG) filtering, Wavelet denoising, and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULC classification. Savitzky-Golay filtering yielded the most efficient results, increasing classification accuracy from 0.71 (noisy) to 1.00 (denoised), resulting in perfect classification with zero errors and enhancing the More >