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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. · 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.

Neeta Bajpai, Rahul Pethe · 0 citations
Preprint Aug 2026

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. · 0 citations
Oct 2026

Automated Landslide Scar Detection in the Himalayan Region Using Satellite Imagery and YOLO-Based Deep Learning Models

The results indicate that YOLOv8 achieved the highest accuracy (F-score: 0.857), followed by YOLOv5, YOLOv7, and YOLOv6, which highlight the potential of YOLO-based models for rapid and accurate landslide detection, contributing to efficient hazard assessment and disaster response efforts.

N. Chandra, Himadri Vaidya, K. Abhinav · 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. · 0 citations

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