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 >

Binghong Zhang, Jialing Zhou, Xinye Zhou, Jia Zhao, Jinchun Zhu, Guangpeng Fan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068309 - 10 November 2025

Abstract Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring, urban planning, and disaster assessment. However, traditional methods exhibit deficiencies in detail recovery and noise suppression, particularly when processing complex landscapes (e.g., forests, farmlands), leading to artifacts and spectral distortions that limit practical utility. To address this, we propose an enhanced Super-Resolution Generative Adversarial Network (SRGAN) framework featuring three key innovations: (1) Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing; (2) A multi-loss joint optimization strategy… More >

Jia Liu¹, Hao Chen¹, Hang Gu¹, Yushan Pan^2,3, Haoran Chen¹, Erlin Tian⁴, Min Huang⁴, Zuhe Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068162 - 10 November 2025

Abstract Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning, disaster emergency response, and resource management. However, existing methods face challenges such as spectral similarity between buildings and backgrounds, sensor variations, and insufficient computational efficiency. To address these challenges, this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network (MewCDNet), which integrates the advantages of Convolutional Neural Networks and Transformers, balances computational costs, and achieves high-performance building change detection. The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction, integrates multi-level feature maps through a multi-scale fusion… More >

Saurabh Singh^1,2, Sudip Pandey^3,*, Ankush Kumar Jain¹

Revue Internationale de Géomatique, Vol.34, pp. 899-914, 2025, DOI:10.32604/rig.2025.071156 - 09 December 2025

Abstract Urban expansion in semi-arid regions poses critical challenges for sustainable land management, ecological resilience, and heritage conservation. Jaipur, India—a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage City located in a semi-arid environment—faces rapid urbanization that threatens agricultural productivity, fragile ecosystems, and cultural assets. This study quantifies past and projects future land use/land cover (LULC) dynamics in Jaipur to support evidence-based planning. Using the Dynamic World dataset, we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary. Temporal change detection was conducted through empirical transition probability analysis, and future… More >

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 >