Abstract
Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.
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Funding
This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202301205, KJQN202001218, KJQN202301260, KJQN202101206, KJQN202201238), the Research development and application of “big data intelligent prediction and early warning cloud service platform for geological disasters in the Three Gorges Reservoir Area” of Chongqing Municipal Education Commission (Grant No. HZ2021012), the Natural Science Foundation of Chongqing, China (Grant No. CSTB2023NSCQ-MSX0433), the Chongqing Engineering Research Center of Disaster Prevention & Control for Banks and Structures in Three Gorges Reservoir Area (Grant No. SXAPGC21ZD01, SXAPGC24XC20, SXAPGC23XC02), the Science and technology innovation project of Chongqing Wanzhou District Bureau of science and technology (Grant No. wzstc20230303) and 2024 Graduate Student Research and Innovation Program at Chongqing Three Gorges College (Grant No. YJSKY24049).
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Lin was primarily responsible for conceptualizing the entire article and writing the introduction and methods; Li and Qiang were mainly responsible for obtaining funding for the article and proofreading the article; Xu is primarily responsible for using the software; Liang was responsible for writing Sections "Study area profiles and Data sources, Results and Analysis , Discussions and Conclusions"; Chen, Yang and Zhang were responsible for collecting research data and drawing all tables and pictures for the article.
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Communicated by: Hassan Babaie
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Lin, H., Li, L., Qiang, Y. et al. A method for landslide identification and detection in high-precision aerial imagery: progressive CBAM-U-net model. Earth Sci Inform 17, 5487–5498 (2024). https://doi.org/10.1007/s12145-024-01465-6
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DOI: https://doi.org/10.1007/s12145-024-01465-6