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

Chong Niu, Kebo Ma, Xiaoyong Shen, Xiaoming Wang, Xiao Xie (), Lin Tan and Yong Xue
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Chong Niu: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Kebo Ma: Rizhao Marine and Fishery Research Institute, Rizhao 276800, China
Xiaoyong Shen: Shandong Province Institute of Land Surveying and Mapping, Jinan 250013, China
Xiaoming Wang: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Xiao Xie: Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Lin Tan: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Yong Xue: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Land, 2023, vol. 12, issue 2, 1-12

Abstract: 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.

Keywords: landslide detection; mudslide detection; attention; convolutional neural networks; remote sensing; multi-scale detection (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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