Naveen Chandra¹, Himadri Vaidya^2,3, Suraj Sawant⁴, Shilpa Gite^5,6, Biswajeet Pradhan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3351-3375, 2025, DOI:10.32604/cmes.2025.064395 - 30 June 2025

Abstract Landslide hazard detection is a prevalent problem in remote sensing studies, particularly with the technological advancement of computer vision. With the continuous and exceptional growth of the computational environment, the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning. Furthermore, attention models, driven by human visual procedures, have become vital in natural hazard-related studies. Hence, this paper proposes an enhanced YOLOv5 (You Only Look Once version 5) network for improved satellite-based landslide detection, embedded with two popular attention modules: CBAM (Convolutional Block Attention Module) More >

Haoyang Tang^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1485-1506, 2025, DOI:10.32604/cmc.2025.064441 - 09 June 2025

Abstract Semantic segmentation provides important technical support for Land cover/land use (LCLU) research. By calculating the cosine similarity between feature vectors, transformer-based models can effectively capture the global information of high-resolution remote sensing images. However, the diversity of detailed and edge features within the same class of ground objects in high-resolution remote sensing images leads to a dispersed embedding distribution. The dispersed feature distribution enlarges feature vector angles and reduces cosine similarity, weakening the attention mechanism’s ability to identify the same class of ground objects. To address this challenge, remote sensing image information granulation transformer for… More >

Hou Jiang^*

Revue Internationale de Géomatique, Vol.34, pp. 333-334, 2025, DOI:10.32604/rig.2025.067517 - 05 June 2025

Abstract This article has no abstract. More >

Elhoucein Layati^*, Omaima Elkbichi, Bahija Choukri, Mohamed El Ghachi

Revue Internationale de Géomatique, Vol.34, pp. 277-300, 2025, DOI:10.32604/rig.2025.063846 - 30 May 2025

Abstract Awareness of the impact of climate change, urbanization, population growth, and anthropogenic pressure on surface waters has led to the need for specialized studies on groundwater potential. Groundwater is an important source of freshwater, particularly in regions where surface water is scarce. With climate change, the need to rely on these waters to cope with water shortages and rising demand is becoming increasingly apparent. Remote sensing, the Analytic Hierarchy Process (AHP), and the Geographic Information System (GIS) are advanced spatial tools used in this study to assess groundwater potential in the Oued Lakhdar watershed, which… More >

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2751-2771, 2025, DOI:10.32604/cmc.2025.060860 - 16 April 2025

Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >

Abdel Aziz Osseni¹, Hubert Olivier Dossou-Yovo^2,*, Apollon D.M.T. Hegbe³, Muhammad Nauman Khan⁴, Brice Sinsin²

Revue Internationale de Géomatique, Vol.34, pp. 209-234, 2025, DOI:10.32604/rig.2025.061448 - 14 April 2025

Abstract Today, environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent sustainable decision tools. The Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs, habitats for wildlife conservation lack substantial investigations on the vegetation dynamics. Despite the protection measures they benefit from, these reserves remain dependent on climatic hazards that can influence their stability. The present study is innovative since it applied remote sensing techniques combined with climate records from the last thirty years to… More >

Mohammad Barr^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 593-616, 2025, DOI:10.32604/cmes.2025.062264 - 11 April 2025

Abstract Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance and management. However, challenges like small object detection, scale variation, and the presence of closely packed objects in these images hinder accurate detection. Additionally, the motion blur effect further complicates the identification of such objects. To address these issues, we propose enhanced YOLOv9 with a transformer head (YOLOv9-TH). The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms. We… More >

Jia Liu¹, Hang Gu¹, Fangmei Liu¹, Hao Chen¹, Zuhe Li¹, Gang Xu², Qidong Liu², Wei Wang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025

Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection More >

Vu Thi Phuong¹, Bui Bao Thien^2,*

Revue Internationale de Géomatique, Vol.34, pp. 151-168, 2025, DOI:10.32604/rig.2025.062829 - 21 March 2025

Abstract Vegetation is crucial to ecosystems, thus, detecting and assessing changes in vegetation cover are receiving increasing attention. In this study, we combine remote sensing data and geographic information systems to assess vegetation cover changes in Da Nang city, Vietnam, between 1988 and 2022. Remote sensing images for the years 1988, 2000, and 2010 were obtained from Landsat 5-TM satellite data, and imagery for 2022 was obtained from Landsat 9-OLI/TIRS satellite data. In each satellite scene, we used supervised classification and spectral indices (NDWI—Normalized Difference Water Index, NDVI—Normalized Difference Vegetation Index, and SAVI—Soil Adjusted Vegetation Index) More >

Qi Sheng¹, Huiqin Ma^1,*, Jingcheng Zhang¹, Zhiqin Gui¹, Wenjiang Huang^2,3, Dongmei Chen¹, Bo Wang¹

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 421-440, 2025, DOI:10.32604/phyton.2025.060152 - 06 March 2025

Abstract Yellow rust (Puccinia striiformis f. sp. Tritici, YR) and fusarium head blight (Fusarium graminearum, FHB) are the two main diseases affecting wheat in the main grain-producing areas of East China, which is common for the two diseases to appear simultaneously in some main production areas. It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space, conduct detailed disease severity monitoring, and scientific control. Four images on different dates were acquired from Sentinel-2, Landsat-8, and Gaofen-1 during the critical period of winter wheat, and 22 remote sensing features that… More >