基于MultiU-EGANet模型的同震滑坡智能识别

张灿灿; 丁明涛; 申传庆; 李云龙; 李振洪; 余琛

doi:10.3799/dqkx.2025.067

Volume 50 Issue 8

Aug. 2025

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Article Navigation > Earth Science > 2025 > 50(8): 3182-3198

Zhang Cancan, Ding Mingtao, Shen Chuanqing, Li Yunlong, Li Zhenhong, Yu Chen, 2025. Intelligent Recognition of Coseismic Landslides Based on MultiU-EGANet Model. Earth Science, 50(8): 3182-3198. doi: 10.3799/dqkx.2025.067

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Zhang Cancan, Ding Mingtao, Shen Chuanqing, Li Yunlong, Li Zhenhong, Yu Chen, 2025. Intelligent Recognition of Coseismic Landslides Based on MultiU-EGANet Model. Earth Science, 50(8): 3182-3198. doi: 10.3799/dqkx.2025.067

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Intelligent Recognition of Coseismic Landslides Based on MultiU-EGANet Model

doi: 10.3799/dqkx.2025.067

Zhang Cancan^{1, 2
,
,},
Ding Mingtao^{1, 2, 3, 4, 5
,
,
,},
Shen Chuanqing⁶,
Li Yunlong^{1, 2},
Li Zhenhong^{1, 2, 3, 4},
Yu Chen^{1, 2, 3, 4}

1.
College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
2.
Big Data Center for Geosciences and Satellites (BDCGS), Chang'an University, Xi'an 710054, China
3.
Key Laboratory of Loess, Xi'an 710054, China
4.
Key Laboratory of Western China's Mineral Resource and Geological Engineering, Ministry of Education, Xi'an 710054, China
5.
Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, Laboratory of smart Earth, Beijing 100029, China
6.
Shaanxi Geomatics Center of Ministry of Natural Resources, Xi'an 710054, China

Received Date: 2025-02-13
Publish Date: 2025-08-25

Abstract

Abstract

Coseismic landslide mapping plays a crucial role in emergency response and disaster assessment. To improve landslide identification, this paper proposes a novel and enhanced model, MultiU-EGANet. The model is built upon the U-Net architecture as the baseline, with the introduction of the MultiRes module to extract feature information across multiple scales. Additionally, the Edge-Guided Attention (EGA) module is incorporated to enhance the delineation of landslide boundaries using the Laplace operator, thereby improving the segmentation accuracy at the boundaries. A composite loss function, combining Dice loss and Focal loss, is designed to further enhance the model's robustness. Using landslide data from the Jiuzhaigou area, experimental results demonstrate that the proposed model significantly improves landslide identification accuracy compared to the baseline model. Furthermore, comparative experiments conducted with landslide data from Hokkaido show that the proposed method outperforms existing models in landslide identification tasks, with F1 scores increasing by 33.31%, 5.45%, 2.31%, and 2.18%, respectively. These results validate the effectiveness of the proposed method for coseismic landslide identification.
- coseismic landslide,
- change detection,
- multiscale,
- edge-guided,
- engineering geology

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References(44)

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