Automated Landslide Mapping in Japan using the Segformer Model: Enhancing Accuracy and Efficiency in Disaster Management

Abstract

Japan is known for its landslide susceptibility due to its steep topography, high seismic activity, and heavy rainfall patterns. These landslides have resulted in significant loss of life and infrastructure damage. To effectively manage landslide risks, accurate mapping of landslide-prone areas is essential. This research focuses on enhancing landslide mapping in Japan using an automated system based on the Segformer model, which combines Transformers and MLP decoders for semantic segmentation. A dataset of aerial images from various regions in Japan was used to train and evaluate the model. The Segformer model achieved a Mean Accuracy of 0.85, a Mean IoU of 0.80, loss value of 0.13 with a recall and precision value of 0.92, respectively, demonstrating its effectiveness in identifying regions prone to landslides. By leveraging advanced algorithms and data analysis techniques, the automated system improves the efficiency and accuracy of landslide mapping efforts. The research findings significantly impact proactive disaster management and mitigation strategies in Japan.