ShapeFormer: A Shape-Enhanced Vision Transformer Model for Optical Remote Sensing Image Landslide Detection

Abstract

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 paper, to better extract and keep the multi-scale 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 pixelwise 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.

EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images

“…The primary dataset’s study area is located in Bijie City, situated in the northwestern part of Guizhou Province, China. The Bijie dataset’s imagery, acquired by the TripleSat satellite from May to August 2018, has a spatial resolution of 0.8 m. Most of the landslides in this dataset were triggered by rainfall, earthquakes and human activities [ 42 ]. The landslide types include debris avalanches, rock slides and rockfalls.…”

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

“…With M S(I ori ), this paper proposes a channelwise fusion structure to obtain diversified evidence set. Denote ⊗ as the Hadamard multiplier to channel-wisely fuse the multiscale visual saliency to the remote sensing landslide images: (14) where C ∈ {H, S, V } represents the image channels.…”

“…In this realm, [13] associates attention mechanism with convolutional neural networks for optical remote sensing landslide segmentation. [14] also proposes a ShapeFormer for the same task. Except the mentioned works, many state-of-the-art learning techniques, including multiclass classification [15] and few-short learning [16], are also found effective.…”

Optimal and Multi-View Strategic Hybrid Deep Learning for Old Landslide Detection in the Loess Plateau, Northwest China

“…Tang et al [44] proposed the transformerbased semantic segmentation model (SegFormer) to identify coseismic landslides, and this has better performance compared with CNNs in landslides detection. Lv et al [45] proposed a pyramid vision transformer (PVT) model for landslides detection, which directly models the global information of different scales in remote sensing images. These transformer-based models can detect landslides well with the advantage that they can learn global features better.…”