TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model

arXiv:2403.10127v1 Announce Type: new
Abstract: Landslides are one of the most destructive natural disasters in the world, posing a serious threat to human life and safety. The development of foundation models has provided a new research paradigm for large-scale landslide detection. The Segment Anything Model (SAM) has garnered widespread attention in the field of image segmentation. However, our experiment found that SAM performed poorly in the task of landslide segmentation. We propose TransLandSeg, which is a transfer learning approach for …

abstract arxiv cs.cv detection development foundation foundation model human life natural natural disasters paradigm research safety sam scale segment segment anything segment anything model segmentation semantic threat transfer transfer learning type vision world