TransLandSeg: A Transfer Learning Approach for Landslide Semantic
  Segmentation Based on Vision Foundation Model
Changhong Hou Junchuan Yu Daqing Ge Liu Yang Laidian Xi Yunxuan Pang Yi Wen
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 landslide semantic
segmentation based on a vision foundation model (VFM). TransLandSeg outperforms
traditional semantic segmentation models on both the Landslide4Sense dataset
and the Bijie landslide dataset. Our proposed adaptive transfer learning (ATL)
architecture enables the powerful segmentation capability of SAM to be
transferred to landslide detection by training only 1.3% of the number of the
parameters of SAM, which greatly improves the training efficiency of the model.
Finally we also conducted ablation experiments on models with different ATL
structures, concluded that the deployment location and residual connection of
ATL play an important role in TransLandSeg accuracy improvement.