Work

Deep learning-based landslides detection improved by the gray level co-occurrence matrix of RGB optical images

Year: 2026

Type: article

Abstract: Landslides, particularly those induced by rainfall, are among the most devastating natural hazards, often causing severe disruptions to transportation infrastructure and hindering emergency rescue operations in mountainous regions. This study presents a method to improve the accuracy of landslide detection using RGB remote sensing imagery by integrating texture features derived from the gray level co-occurrence matrix (GLCM). A hybrid framework was developed that incorporates GLCM-based texture descriptors into deep learning models, including U-Net, DeepLabv3+, and Mask R-CNN, enabling the automatic extraction and recognition of complex spatial patterns. Experimental results demonstrate that the integration of texture features substantially enhances detection performance, with average improvements of 15.2% in intersection over union (IoU), 7.1% in F 1-score, and 9.9% in sensitivity (SE) across the three models compared to RGB-only inputs. These findings confirm the effectiveness of texture-assisted deep learning and highlight its potential as a robust and cost-efficient solution for precise landslide detection, offering significant advantages for geological hazard monitoring and emergency response.

Source: Journal of Rock Mechanics and Geotechnical Engineering

DOI: 10.1016/j.jrmge.2025.09.041

Authors: Haijia Wen, Jiayi Li, Jingyuan Yu, Junhao Huang, Bo Liu

Institutions: Chongqing University, Tsinghua University

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Key Technologies Research and Development Program, National Key Research and Development Program of China

Award: G3151790853