LAUSR

Landslides pose a serious threat to human life, safety, and natural resources. Remote sensing images can be used to effectively monitor landslides at a large scale, which is of great significance for pre-disaster warning and post-disaster assistance. In recent years, deep learning-based methods have made great progress in the field of remote sensing image landslide detection. In remote sensing images, landslides display a variety of scales and shapes. In this article, to better extract and keep the multiscale shape information of landslides, a shape-enhanced vision transformer (ShapeFormer) model is proposed. For the feature extraction, a pyramid vision transformer (PVT) model is introduced, which directly models the global information of local elements at different scales. To learn the shape information of different landslides, a shape feature extraction branch is designed, which uses the adjacent feature maps at different scales in the PVT model to improve the boundary information. After the feature extraction step, a decoder with deconvolutional layers follows, which combines the multiple features and gradually recovers the original resolution of the combined features. A softmax layer is connected with the combined features to acquire the final pixel-wise result. The proposed ShapeFormer model was tested on two public datasets—the Bijie dataset and the Nepal dataset—which have different spectral and spatial characteristics. The results, when compared with those of some of the state-of-the-art methods, show the potential of the proposed method for use with multisource optical remote sensing data for landslide detection.