Compact convolutional transformer for landslide susceptibility mapping

High-quality landslide susceptibility mapping (LSM) is an important step in landslide disaster prevention and mitigation. In order to explore and evaluate the practical performance of a convolutional and transformer hybrid model in landslide susceptibility mapping, this study selected 202 historical landslides and seven conditioning factors to construct a geographical spatial dataset for LSM. The hybrid model called the compact convolutional transformer (CCT) model was used to perform LSM in the Three Gorges Reservoir area in China, and it was compared with vision transformer (ViT), convolutional neural network (CNN), and residual network (ResNet). The results show that the CCT model has the best overall performance, achieving the highest accuracy in multiple statistical metrics, and improving the accuracy of LSM. This study provides a new approach for obtaining high-quality LSM using a convolutional and transformer hybrid model.