Landslide Susceptibility Mapping Considering Landslide Local-Global Features Based on CNN and Transformer

Landslide susceptibility mapping (LSM) is a crucial step in quantitatively assessing landslide risk, essential for geologic hazards prevention. With the rapid development of deep learning models, convolutional neural networks (CNNs), and transformer architectures have been applied to LSM. However, these models still face the challenges of suboptimal mapping accuracy and limited capacity for multilevel landslide features extraction. In this study, we present a CNN-transformer local-global features extraction network (CTLGNet) that combines the strengths of both CNN and transformer models to effectively extract both landslide local and global features. We apply this model to LSM in two regions: the Three Gorges Reservoir area and Jiuzhaigou. To begin, nine landslide conditioning factors are selected and analyzed to construct the landslide dataset for LSM. Subsequently, the dataset is randomly split into training, validation, and test datasets in a 6:2:2 ratio to attain LSM results. Then, CTLGNet is compared to CNN, residual neural network, densely connected convolutional network, vision transformer, and fractional Fourier image transformer using various evaluation metrics. The results demonstrate that CTLGNet exhibits exceptional landslide prediction and generalization capabilities, outperforming the other five models across all evaluation metrics except Recall, with AUC values of 0.9817 and 0.9693 for the two regions, respectively. The LSM results indicate that CTLGNet can effectively extract both landslide local and global features to achieve landslide localization and detail capture. Overall, our proposed framework excels in extracting multilevel landslide features and holds great potential for widespread application.