Skip to content

Deep Learning for Landslide Risk Assessment: A Novel Satellite Image Analysis Framework

Sep 2026 · Sensing and Imaging · Vol 27 · 0 citations · 19 references

TL;DR

This study showcases the power of deep learning technology in automating landslides’ detection processes and provides a basis for incorporating the technique into the disaster monitoring system framework.

View source

Similar papers

Conference Aug 2026

InSAR-Guard: Lightweight AI Framework for Landslide Hazard Detection

Landslides are a type of natural disaster that causes massive loss of life and property. Continuous observation of ground deformation is necessary for early warning and mitigation of risks. Interferometric Synthetic Aperture Radar (InSAR) technology facilitates large-scale, all-weather observation of ground deformation...

D. Chandran, A. N, Adhithya P. Nair 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
Open access Sep 2026

Automated detection of bridge and road damage in orthophotos using deep learning

This study proposes a binary deep learning framework to determine whether the bridges or roads in remote sensing data have been damaged, and provides an automated method for image analysis based on remote sensing data.

Xianfeng Li, Jie-An Liang, Shi-Tao Zheng et al. · 0 citations
Open access Sep 2026

Hybrid Deep Learning Framework for Landslide Risk Prediction Using Meteorological and Geospatial Parameters

Landslide prediction plays an important role in disaster risk reduction and protecting infrastructure and human lives when performed accurately and in a timely manner. Despite good performance, machine learning techniques are hindered by severe class imbalance in landslide datasets and the opacity of complex predictive...

Mohsin Hassan, A. Kotwal, J. Manhas 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

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.