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Open access
Aug 2026
ChangeFormer-Based Detection of Landslide-Damaged Areas Using Sentinel-2 Imagery in South Korea
Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normali...
Geonhwi Jung, Mooyoung Lim, C. Woo et al.
· Land · 0 citations
Review
Open access
Sep 2026
Landslide Segmentation from Multispectral Remote Sensing Data Using U-Net with Multichannel Topographic Information
A deep learning-based semantic segmentation framework using the U-Net architecture for pixel-level landslide delineation from multispectral and topographical remote sensing data and establishes a foundation for further investigation of advanced Transformer- and attention-based architectures is presented.
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Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihoo...
Luigi Russo, A. Ferral, S. Ullo et al.
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Oct 2026
Automated Landslide Scar Detection in the Himalayan Region Using Satellite Imagery and YOLO-Based Deep Learning Models
Landslides in the Himalayan region are the most serious hazards due to their destructive consequences in the context of life and the economy. More landslides are anticipated based on the present environmental and climatic scenario. Hence, a landslide information detection model is the foremost for hazard risk assessm...
N. Chandra, Himadri Vaidya, K. Abhinav
· Journal of the Geological So... · 0 citations
Review
Open access
Sep 2026
Identification of Landslide Risks in the Subtropical Hilly Regions of Southern China Using Integrated Multi-Source Synthetic Aperture Radar Interferometry and Machine Learning
The subtropical hilly regions of southern China are characterized by dense vegetation and highly concealed landslides, making it difficult for traditional, single-source remote sensing methods to meet disaster prevention needs. The core scientific contribution of this study is the development of a hierarchical, progres...
Guan-Zhi Luo, Qi Zhan, Fei-Ting Yi et al.
· Applied Sciences · 0 citations
Open access
Sep 2026
Landslide Identification Based on Diverse Remote-Sensing Datasets and Improved Deep Learning Models
Two improved models enhanced the accuracy of landslide identification and resistance to interference, demonstrating potential for landslide monitoring and emergency response.
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· Remote Sensing · 0 citations