A Graph-Transformer Method for Landslide Susceptibility Mapping

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.14556-14574
• Main Authors: Zhang, Qing, He, Yi, Zhang, Yalei, Lu, Jiangang, Zhang, Lifeng, Huo, Tianbao, Tang, Jiapeng, Fang, Yumin, Zhang, Yunhao
• Format: Article
• Language: English
• Subjects: Correlation; Disasters; Environment similarity relationship; Geology; graph; landslide susceptibility mapping (LSM); Rain; Rivers; Terrain factors; transformer; Transformers
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3437751

Landslide susceptibility mapping (LSM) is of great significance for regional land resource planning and disaster prevention and reduction. The machine learning (ML) method has been widely used in the field of LSM. However, the existing LSM model fails to consider the correlation between landslide and disaster-prone environment (DPE) and lacks global information, resulting in a high false alarm rate of LSM. Therefore, we propose an LSM method with graph-transformer that considers the DPE characteristics and global information. First, correlation analysis and importance analysis are employed on nine landslide contributing factors, and the landslide dataset is generated by combining remote sensing image interpretation and field verification. Second, a graph constrained by environment similarity relationship is constructed to realize the correlation between landslide and DPE. Then, the transformer module is introduced to construct a graph-transformer model that considers the global information. Finally, the LSM is generated and analyzed, and the accuracy of the proposed model is compared and evaluated. The experimental results show that the environment similarity relationship graph effectively improves the accuracy of the models and weakens the influence of environmental differences on the models. Compared with graph convolutional network, graph sample and aggregate, and graph attention network models, the area under the curve (AUC) value of the proposed model is more than 2.05% higher under the environment similarity relationship. In addition, the AUC value of the proposed model is more than 8.8% higher than that of traditional ML models. In conclusion, our proposed model framework can get better evaluation results than most existing methods, and its results can provide effective ways and key technical support for landslide disaster investigation and control.