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 >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Yifan Zhang¹, Yong Gan^2,*, Mengke Tang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068880 - 09 December 2025

Abstract High-resolution remote sensing imagery is essential for critical applications such as precision agriculture, urban management planning, and military reconnaissance. Although significant progress has been made in single-image super-resolution (SISR) using generative adversarial networks (GANs), existing approaches still face challenges in recovering high-frequency details, effectively utilizing features, maintaining structural integrity, and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery. To address these limitations, this paper proposes the Improved Residual Module and Attention Mechanism Network (IRMANet), a novel architecture specifically designed for remote sensing image reconstruction. IRMANet builds upon the Super-Resolution… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… More >