Attention-Enhanced Region Proposal Networks for Multi-Scale Landslide and Mudslide Detection from Optical Remote Sensing Images

Detecting areas where a landslide or a mudslide might occur is critical for emergency response, disaster recovery, and disaster cost estimation. Previous works have reported that a variety of convolutional neural networks (CNNs) significantly outperform traditional approaches for landslide/mudslide detection. These approaches always consider features from the local window and neighborhood information. The CNNs mainly focus on the features derived at a local scale, which might be inefficient for recognizing complex landslide and mudslide scenes. To effectively identify landslide and mudslide risks at a local and global scale, this paper integrates attentions into the architecture of state-of-the-art CNNs—including Faster RCNN—to develop an attention-enhanced region proposal network for multi-scale landslide/mudslide detection. In detail, we employed the attentions to process the region proposals generated by a region proposal network and then combined the results obtained from the attentions and region proposal network to identify whether the object included in a region proposal was a landslide/mudslide. Based on our developed dataset and the Bijie dataset, the experimental results prove that: (1) although the state-of-the-art CNNs for object detection can precisely detect landslides and mudslides, they are inadequate in dealing with similarity to non-landslide/non-mudslide regions; and (2) the proposed method, which integrates global features from attention layers into local features derived from CNNs, outperforms the unmodified CNNs in detecting non-landslides and non-mudslides. Our findings prove that the representations at the local and global scale might be significant for precise landslide and mudslide detection.