Landslidenet: Adaptive Vision Foundation Model for Landslide Detection

Recent advancements in Vison Foundation Models (VFMs) like the Segment Anything Model (SAM) have exhibited remarkable progress in natural image segmentation. However, its performance on remote sensing images is limited, especially in some application scenarios that require strong expert knowledge involvement, such as landslide detection. In this study, we proposed an effective segmentation model, namely LandslideNet, which is realized by embedding a tuning layer in a pre-trained encoder and adapting the SAM to the landslide detection scene for the first time. The proposed method is compared with traditional convolutional neural networks (CNN) on two well-known landslide datasets. The results indicate that the proposed model with fewer training parameters has better performance in detecting small-scale targets and delineating landslide boundaries, with an improvement of 6-7 percentage points in accuracy (F1 and mIoU) compared to mainstream CNN-based methods.