Dynamic graph attention networks for point cloud landslide segmentation

Accurate landslide segmentation is crucial for obtaining damage information in disaster mitigation and relief efforts. This study aims to develop a deep learning network for accurate point cloud landslide segmentation. The proposed dynamic graph attention network (DGA-Net) has four steps. First, the down-sampling and neighbor search are applied to generate the samples that effectively represent the relevant landslide information. Second, the edge features of neighbor points are constructed based on graph structure to extract and enhance point cloud features. Third, the attention mechanism assigns adaptive weights to edge features and aggregates them into new point features. Fourth, the graph structure, edge features, and attention weights are dynamically updated through the hierarchical structures, which enable an expanded receptive field. In the upper reach of the Jinsha River, point clouds were prepared for landslide segmentation. The controlled experiments were designed for effectiveness evaluation. The results reported that proposed DGA-Net achieved the highest mean Intersection over Union (mIoU) of 0.743 and F1-score of 0.786, which was over 6.7% and 3.6% mIoU higher than shallow machine learning and other deep learning models. Besides, we analyzed the effect of super parameters in sampling strategy and the segmentation threshold in prediction stage on the model performance. The results showed that the samples with suitable sampling diameters and appropriate neighboring points are beneficial for landslide segmentation, and using optimal thresholds to segment stacked multiple prediction values can improve mIoU by 6%. Furthermore, the visualized feature maps revealed that the proposed model can index landslide points in feature space, which is beneficial to construct graph structures and use attention to enhance features. Comparative studies on the above experiments proved the superiority of the proposed method for landslide segmentation. We hope that our method and research results can contribute to post-disaster relief efforts.