Application of Transformer Models to Landslide Susceptibility Mapping

Abstract

Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information and do not retain spatial information. This is a property of the CNN itself, resulting in low accuracy of LSM. Based on the above problems, we use Vision Transformer (ViT) and its derivative model Swin Transformer (Swin) to conduct LSM for the selected study area. Machine learning and a CNN model are used for comparison. Fourier transform amplitude, feature similarity and other indicators were used to compare and analyze the difference in the results. The results show that the Swin model has the best accuracy, F1-score and AUC. The results of LSM are combined with landslide points, faults and other data analysis; the ViT model results are the most consistent with the actual situation, showing the strongest generalization ability. In this paper, we believe that the advantages of ViT and its derived models in global feature extraction ensure that ViT is more accurate than CNN and machine learning in predicting landslide probability in the study area.

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

“…This result underscores the model's enhanced capability in predicting landslide susceptibility, showcasing its superiority over conventional methods. Bao et al (2022) [124] tackled the limitations of traditional CNNs, which predominantly focus on local information due to their fixed convolutional kernels. To address this, they proposed a novel LSM approach that integrates the Vision Transformer (ViT) and Swin Transformer, which enables a more comprehensive understanding of global spatial information.…”

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

“…Qualitative methods are mostly based on experience, such as the analytic hierarchy process (AHP) method [18], historical geological hazard inventory analysis [19], and expert scoring methods [20]. These methods are simple and cost-effective, but less objective and only suitable for the analysis of smaller study areas [21]. With the development of geographic information data and computer technology, quantitative methods have gradually become the mainstream method in the assessment of geological hazards [22].…”