Khlieeq Ul Zaman^1,2,*, Ahmad Saeed³, Muhammad Awais Khan², Hafiz Abdul Basit⁴, Shaharyar², Maria Anum², Rani Ummay Farwa^2,*, Mahmood Iqbal¹, Ali Raza²

Revue Internationale de Géomatique, Vol.35, pp. 409-422, 2026, DOI:10.32604/rig.2026.083214 - 02 July 2026

Abstract Groundwater is the primary buffer against water scarcity in rapidly urbanizing regions, yet its sustainable management is constrained by limited hydrogeological data. This study presents an integrated Geographic Information System (GIS) remote sensing framework strengthened with Artificial Intelligence (AI) to delineate groundwater potential zones (GWPZs) in the Islamabad Capital Territory. Six thematic layers—slope, drainage density, lithology, rainfall, land use/land cover (LULC), and the Normalized Difference Vegetation Index (NDVI)—were derived from SRTM DEM, Sentinel-2 imagery, geological maps, and climate records. Each layer was standardized, reclassified, and weighted using the Analytical Hierarchy Process (AHP). A complete pairwise… More >

Mahmood Ashraf^1,2, Nuha Zamzami³, Shtwai Alsubai⁴, Raed Alharthi⁵, Muhammad Umer^6,*, Yunyoung Nam⁷, Yongwon Cho^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.078738 - 30 June 2026

Abstract Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information… More >

Shuaiyu Zhu¹, Sergey Ablameyko^1,2, Ji Li^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082486 - 15 June 2026

Abstract Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the More >

Li Chen¹, Fan Zhang^2,*, Guangwei Xie³, Yanzhao Gao¹, Xiaofeng Qi¹, Mingqian Sun²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078314 - 15 June 2026

Abstract Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into… More >

Jesús Rodrigo-Comino^1,*, María Teresa González-Moreno¹, Lucía Moreno-Cuenca¹, Laura Cambronero-Ruiz¹, Clemente Irigaray², Francisco Serrano Bernardo³, Víctor Hugo Durán Zuazo⁴, Jesús Fernández-Gálvez⁵, Andrés Caballero-Calvo⁵, Víctor Rodríguez-Galiano⁶

Revue Internationale de Géomatique, Vol.35, pp. 333-349, 2026, DOI:10.32604/rig.2026.081503 - 11 June 2026

Abstract Soil degradation in Mediterranean agricultural systems is strongly conditioned by topography, water redistribution and solar exposure, factors that can be effectively studied using very high-resolution remote sensing. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)-derived geomorphometry combined with machine learning techniques to analyse the spatial variability of the Normalized Difference Vegetation Index (NDVI) as a surface spectral response under post-harvest conditions in a Mediterranean cereal field affected by soil degradation and gully erosion, located near Casabermeja (Málaga, southern Spain). High-resolution RGB and multispectral UAV data were used to generate a Digital Terrain Model… More >

Fayaz Ullah Shinwari¹, Mumtaz Ali Khan^2,*, Saad Khan^3,4, Rizwan Niaz⁵, Mansour Almazroui^6,7

Revue Internationale de Géomatique, Vol.35, pp. 273-290, 2026, DOI:10.32604/rig.2026.080586 - 21 May 2026

Abstract Groundwater is an essential resource contributing substantially to the annual total water supply. It enables agricultural irrigation and provides billions of people with their main source of drinking water. But overuse of groundwater has decreased its supply and, in certain places, resulted in soil subsidence. In the complex hydrogeological terrain of Timergara, traditional groundwater exploration is challenging and costly, requiring more efficient mapping approaches. Groundwater recharge potential zones must be assessed in order to guarantee sustainable groundwater management. This study uses Remote Sensing (RS) and Geographic Information System (GIS) methodologies to evaluate groundwater potential sites… More >

Mihaela Tsvetkova, Milen Chanev, Lachezar Filchev^*

Revue Internationale de Géomatique, Vol.35, pp. 179-203, 2026, DOI:10.32604/rig.2026.079622 - 19 May 2026

Abstract This study addresses the challenges of traditional forest inventory methods for Norway spruce (Picea abies (L.) Karst.) by leveraging Sentinel-2 multispectral data to derive critical forest parameters, including biomass, stand density, and site class. Remote sensing offers scalable solutions for large-scale monitoring, yet topographic variability and spectral saturation limit the use of empirical vegetation index (VI)-based approaches. The methodology analyzed 43 Norway spruce subcompartments in Bulgaria’s Parangalitsa Reserve using a 2017 Sentinel-2 L2A scene, calculating 24 vegetation indices (e.g., Canopy Chlorophyll Content Index (CCCI), Forest Cover Index (FCI1/FCI2), Normalized Difference Water Index (NDWI) and three biophysical… More >

Jhoreene Julian¹, Jerome Gacu^2,3,4,*

Revue Internationale de Géomatique, Vol.35, pp. 205-247, 2026, DOI:10.32604/rig.2026.078160 - 19 May 2026

Abstract Surface water quality is a sensitive global environmental issue, as it is important for long-term economic development and environmental sustainability. Due to population growth, urbanization, and the effects of climate change, the degradation of surface water quality cannot be avoided. Therefore, a more accurate, continuous, and operational monitoring of water quality is highly significant. This study aims to systematically review and synthesize existing literature on the technological advancement, challenges, and future directions of Remote Sensing (RS) and Geographic Information System (GIS) techniques in surface water quality monitoring. Following PRISMA guidelines, a structured literature search of… More >

Mohamed ElKashouty^1,*, Mohd Yawar Ali Khan^1,*, Samyah Salem Refadah²

Revue Internationale de Géomatique, Vol.35, pp. 161-177, 2026, DOI:10.32604/rig.2026.079702 - 30 April 2026

Abstract Water scarcity is a significant challenge in arid and semi-arid countries, underscoring the importance of thoroughly studying groundwater resources. Egypt, especially in the Darb El-Arbaein region of the southern Western Desert, faces various water challenges and relies primarily on groundwater from the Nubian Sandstone aquifer. Proper management of this groundwater is essential for addressing these challenges. The study examines the spatial and temporal variations in the hydrogeochemistry of the Nubian sandstone aquifer. Data collected from the aquifer’s monitoring network include key hydrogeochemical parameters, such as total dissolved solid (TDS) and piezometric heads, over different periods.… More >

Leyu Dai^1,2,3, Jindong Wang^1,2,3, Ming Zhou^1,2,3, Song Guo^1,2,3, Hengwei Zhang^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072449 - 10 February 2026

Abstract In recent years, with the rapid advancement of artificial intelligence, object detection algorithms have made significant strides in accuracy and computational efficiency. Notably, research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images (ORSIs). However, in the realm of adversarial attacks, developing adversarial techniques tailored to Anchor-Free models remains challenging. Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures. Furthermore, the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks. This study presents… More >