融合多源特征的轻量化深度学习跨场景滑坡智能识别

邢珂; 窦杰; 何雨健; 晏培修; 杨涛; 李喜; 董傲男

doi:10.3799/dqkx.2025.255

Volume 51 Issue 2

Feb. 2026

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Article Navigation > Earth Science > 2026 > 51(2): 657-673

Xing Ke, Dou Jie, He Yujian, Yan Peixiu, Yang Tao, Li Xi, Dong Aonan, 2026. Lightweight Deep Learning for Cross-Scene Landslide Intelligent Recognition with Multi-Source Feature Fusion. Earth Science, 51(2): 657-673. doi: 10.3799/dqkx.2025.255

Citation:

Xing Ke, Dou Jie, He Yujian, Yan Peixiu, Yang Tao, Li Xi, Dong Aonan, 2026. Lightweight Deep Learning for Cross-Scene Landslide Intelligent Recognition with Multi-Source Feature Fusion. Earth Science, 51(2): 657-673. doi: 10.3799/dqkx.2025.255

Citation:

Xing Ke, Dou Jie, He Yujian, Yan Peixiu, Yang Tao, Li Xi, Dong Aonan, 2026. Lightweight Deep Learning for Cross-Scene Landslide Intelligent Recognition with Multi-Source Feature Fusion. Earth Science, 51(2): 657-673. doi: 10.3799/dqkx.2025.255

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Lightweight Deep Learning for Cross-Scene Landslide Intelligent Recognition with Multi-Source Feature Fusion

doi: 10.3799/dqkx.2025.255

Xing Ke^{1
,
,},
Dou Jie^{1, 2, 3
,
,
,},
He Yujian⁴,
Yan Peixiu⁵,
Yang Tao^{3, 6},
Li Xi^{3, 6},
Dong Aonan²

1.
School of Future Technology, China University of Geosciences, Wuhan 430074, China
2.
Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
3.
Hubei Key Laboratory of Resources and Eco-Environment Geology(Hubei Geological Bureau), Wuhan, 430034, China
4.
Chengdu Engineering Corporation Limited, Power Construction Corporation, Chengdu 610072, China
5.
College of Computers and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
6.
Geological Environmental Center of Hubei Province, Wuhan 430034, China

Received Date: 2025-10-13
Publish Date: 2026-02-25

Abstract

Abstract

Regional-scale landslides triggered by extreme environmental factors pose a significant threat to life and property safety. Consequently, advancing the automation of regional landslide identification and enhancing the information transparency of potential hazard zones in complex terrain are paramount for the construction of geological hazard databases and effective risk management.Deep learning methods provide an effective solution, overcoming the problem of insufficient automation in traditional methods. However, existing research primarily focuses on optimizing model architecture and improving training strategies, leaving challenges in the effective fusion of multi-source topographic data and the enhancement of cross-regional identification capability. To address these bottlenecks, this paper proposes ResU-CBNet, a deep learning model with robust cross-regional identification capability. The model integrates a hybrid spatial and channel attention mechanism into the neural network and utilizes a residual network to replace the conventional network structure. The model's performance under multi-scale feature fusion conditions significantly outperforms that of single remote sensing data, specifically showing improvements of 2.1% in PA, 2.6% in CPA, 6.9% in F1_Score, and 2.9% in MIoU.Furthermore, the model validates its cross-scene generalization capability across regions with different scenarios, spectral bands, and spatial distributions, achieving PA and F1_Score performances of 92.8%, 91.3% and 83.2%, 80.0%, respectively. The identification results demonstrate a high degree of consistency with the actual regions.The cross-scene identification method presented here offers a valuable reference for intelligent landslide recognition and risk assessment.
- deep Learning,
- multi-source data,
- attention mechanism,
- landslide recognition,
- cross-scene

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References(50)

References

Baghbani, A., Choudhury, T., Costa, S., et al., 2022. Application of Artificial Intelligence in Geotechnical Engineering: a State-of-the-Art Review. Earth-Science Reviews, 228: 103991. https://doi.org/10.1016/j.earscirev.2022.103991

Chen, H. S., He, Y., Zhang, L. F., et al., 2024. A Multi-Input Channel U-Net Landslide Detection Method Fusing SAR Multisource Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 1215-1232. https://doi.org/10.1109/JSTARS.2023.3339294

Chen, L. C., Papandreou, G., Schroff, F., et al., 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint, 1706.05587. https://doi.org/10.48550/arXiv.1706.05587

Cheng, L. B., Li, J., Duan, P., et al., 2021. A Small Attentional YOLO Model for Landslide Detection from Satellite Remote Sensing Images. Landslides, 18(8): 2751-2765. https://doi.org/10.1007/s10346-021-01694-6

Dong, A. N., Dou, J., Li, C. D., et al., 2024. Accelerating Cross-Scene Co-Seismic Landslide Detection through Progressive Transfer Learning and Lightweight Deep Learning Strategies. IEEE Transactions on Geoscience and Remote Sensing, 62: 4410213. https://doi.org/10.1109/TGRS.2024.3424680

Dou, J., Xiang, Z. L., Xu, Q., et al., 2023. Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation. Earth Science, 48(5): 1657-1674(in Chinese with English abstract).

Dou, J., Xing, K., Wang, L. Z., et al., 2025. Air-Space-Ground Synergistic Observations for Rapid Post-Seismic Disaster Assessment of 2025 Ms6.8 Xigazê Earthquake, Xizang. Journal of Earth Science, 36(4): 1605-1622. https://doi.org/10.1007/s12583-025-0160-2

Dou, J., Yunus, A. P., Bui, D. T., et al., 2020. Improved Landslide Assessment Using Support Vector Machine with Bagging, Boosting, and Stacking Ensemble Machine Learning Framework in a Mountainous Watershed, Japan. Landslides, 17(3): 641-658. https://doi.org/10.1007/s10346-019-01286-5

Dou, J., Yunus, A. P., Bui, D. T., et al., 2019. Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment, 662: 332-346. https://doi.org/10.1016/j.scitotenv.2019.01.221

Fan, R. S., Chen, Y., Xu, Q. H., et al., 2019. A High-Resolution Remote Sensing Image Building Extraction Method Based on Deep Learning. Acta Geodaetica et Cartographica Sinica, 48(1): 34-41(in Chinese with English abstract).

Guo, Z. Z., Cheng, M. C., Wang, Y. G., et al., 2025a. Landslide Hazard Prediction under an Extreme Rainfall Scenario by Considering Multiple Timescale Rainfalls and Effective Recharge. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 19(4): 775-803. https://doi.org/10.1080/17499518. 2025. 2570863 doi: 10.1080/17499518.2025.2570863

Guo, Z. Z., Zeng, T. R., Zhang, Y. H., et al., 2025b. A Novel Hybrid Model Integrating High Resolution Remote Sensing and Stacking Ensemble Techniques for Landslide Susceptibility Mapping: Application to Event-Based Landslide Inventory. Geomorphology, 486: 109886. https://doi.org/10.1016/j.geomorph.2025.109886

He, Y. J., Dou, J., Wang, X. K., et al., 2024. Comparison on the Application of the Software for Image-Free Control UAV Data Processing of Digital Landslide: a Case Study of Huangtupo Landslide in the Three Gorges Reservoir Area. The Chinese Journal of Geological Hazard and Control, 35(5): 160-173(in Chinese with English abstract).

Howard, A., Sandler, M., Chu, G., et al., 2019. Searching for MobileNetV3. IEEE, 2020. https://doi.org/10.1109/ICCV.2019.00140

Ju, Y. Z., Xu, Q., Jin, S. C., et al., 2020. Automatic Object Detection of Loess Landslide Based on Deep Learning. Geomatics and Information Science of Wuhan University, 45(11): 1747-1755(in Chinese with English abstract).

Liu, X. R., Peng, Y. X., Lu, Z. L., et al., 2023. Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data. IEEE Transactions on Geoscience and Remote Sensing, 61: 4500314. https://doi.org/10.1109/TGRS.2022.3233637

Long, J., Shelhamer, E., Darrell, T., 2015. Fully Convolutional Networks for Semantic Segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 7-12, 2015, Boston, MA, USA. IEEE: 3431-3440. https://doi.org/10.1109/CVPR.2015.7298965

Mao, J. Q., He, J., Liu, G., et al., 2023. Landslide Recognition Based on Improved DeepLabV³⁺ Algorithm. Journal of Natural Disasters, 32(2): 227-234(in Chinese with English abstract).

Osanai, N., Yamada, T., Hayashi, S. I., et al., 2019. Characteristics of Landslides Caused by the 2018 Hokkaido Eastern Iburi Earthquake. Landslides, 16(8): 1517-1528. https://doi.org/10.1007/s10346-019-01206-7

Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention: MICCAI 2015. Cham: SpringerInternationalPublishing: 234-241. https://doi.org/10.1007/978-3-319-24574-4_28

Selvaraju, R. R., Cogswell, M., Das, A., et al., 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision (ICCV). October 22-29, 2017, Venice, Italy. IEEE: 618-626. https://doi.org/10.1109/ICCV.2017.74

Shafiq, M., Gu, Z. Q., 2022. Deep Residual Learning for Image Recognition: a Survey. Applied Sciences, 12(18): 8972. https://doi.org/10.3390/app12188972

Tang, H. M., Li, C. D., Hu, W., et al., 2022. What Is the Physical Mechanism of Major Landslides? Earth Science, 47(10): 3902-3903(in Chinese with English abstract).

Wang, H. H., Liu, J., Zeng, S. K., et al., 2024a. A Novel Landslide Identification Method for Multi-Scale and Complex Background Region Based on Multi-Model Fusion: YOLO + U-Net. Landslides, 21(4): 901-917. https://doi.org/10.1007/s10346-023-02184-7

Wang, Q. Y., Tang, H. M., An, P. J., et al., 2024b. Insight into the Permeability and Microstructure Evolution Mechanism of the Sliding Zone Soil: a Case Study from the Huangtupo Landslide, Three Gorges Reservoir, China. Journal of Earth Science, 35(3): 941-954. https://doi.org/10.1007/s12583-023-1828-0

Wang, X. M., Yin, J., Luo, M. H., et al., 2023. Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules. Journal of Earth Science, 34(5): 1596-1615. https://doi.org/10.1007/s12583-021-1505-0

Woo, S., Park, J., Lee, J. Y., et al., 2018. CBAM: Convolutional Block Attention Module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y., eds., Computer Vision-ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, 11211. Springer, Cham. https://doi.org/10.1007/978-3-030-01234-2_1.

Wu, Z. B., Li, H., Yuan, S. X., et al., 2023. Mask R-CNN-Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China. Remote Sensing, 15(20): 4898. https://doi.org/10.3390/rs15204898

Xiang, X. Y., Gong, W. P., Li, S. L., et al., 2024. TCNet: Multiscale Fusion of Transformer and CNN for Semantic Segmentation of Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 3123-3136. https://doi.org/10.1109/JSTARS.2024.3349625

Xing, K., Dou, J., Chen, N. C., et al., 2025. Landslide Feature Interpretation and Stability Assessment Using Optical-LiDAR Synergy and High-Resolution 3D Modeling. Journal of Natural Disasters, 34(3): 119-132(in Chinese with English abstract).

Xing, K., Li, H., Zhang, L. L., et al., 2025. Analysis of Surface Rupture and Seismic Damage Characteristics of 2025 Dingri Ms6.8 Earthquake in Xizang. Safety and Environmental Engineering, 32(2): 20-30(in Chinese with English abstract).

Xu, Q. S., Ouyang, C. J., Jiang, T. H., et al., 2022. MFFENet and ADANet: a Robust Deep Transfer Learning Method and Its Application in High Precision and Fast Cross-Scene Recognition of Earthquake-Induced Landslides. Landslides, 19(7): 1617-1647. https://doi.org/10.1007/s10346-022-01847-1

Xu, Q., Dong, X. J., Zhu, X., et al., 2023. Landslide Collaborative Observation Technology Based on Real Scene 3D View from Space-Air-Ground-Interior Perspective. Journal of Engineering Geology, 31(3): 706-717(in Chinese with English abstract).

Yang, Y. C., Dou, J., Merghadi, A., et al., 2024a. Advanced Prediction of Landslide Deformation through Temporal Fusion Transformer and Multivariate Time-Series Clustering of InSAR: Insights from the Badui Region, Eastern Tibet. IEEE Transactions on Geoscience and Remote Sensing, 62: 4514219. https://doi.org/10.1109/TGRS. 2024.3504241 doi: 10.1109/TGRS.2024.3504241

Yang, Y. H., Miao, Z. L., Zhang, H., et al., 2024b. Lightweight Attention-Guided YOLO with Level Set Layer for Landslide Detection from Optical Satellite Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 3543-3559. https://doi.org/10.1109/JSTARS.2024.3351277

Yang, Z. Q., Xu, C., 2022. Efficient Detection of Earthquake-Triggered Landslides Based on U-Net++: an Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw=6.6 Earthquake. Remote Sensing, 14(12): 2826. https://doi.org/10.3390/rs14122826

Yin, Y. P., Gao, S. H., 2024. Research on High-Altitude and Long-Runout Rockslides: Review and Prospects. The Chinese Journal of Geological Hazard and Control, 35(1): 1-18(in Chinese with English abstract).

Zhang, S. S., Li, Q. C., Li, H., et al., 2025. Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model. Earth Science, 50(8): 3132-3143(in Chinese with English abstract).

Zhang, Z. X., Liu, Q. J., Wang, Y. H., 2018. Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5): 749-753. https://doi.org/10.1109/LGRS.2018.2802944

窦杰, 向子林, 许强, 等, 2023. 机器学习在滑坡智能防灾减灾中的应用与发展趋势. 地球科学, 48(5): 1657-1674. doi: 10.3799/dqkx.2022.419

范荣双, 陈洋, 徐启恒, 等, 2019. 基于深度学习的高分辨率遥感影像建筑物提取方法. 测绘学报, 48(1): 34-41.

何雨健, 窦杰, 王协康, 等, 2024. 国内外免像控无人机航测软件在数字滑坡中的应用效果对比: 以三峡库区黄土坡滑坡为例. 中国地质灾害与防治学报, 35(5): 160-173.

巨袁臻, 许强, 金时超, 等, 2020. 使用深度学习方法实现黄土滑坡自动识别. 武汉大学学报(信息科学版), 45(11): 1747-1755.

毛佳琪, 何敬, 刘刚, 等, 2023. 基于改进的DeepLabV³⁺算法滑坡识别. 自然灾害学报, 32(2): 227-234.

唐辉明, 李长冬, 胡伟, 等, 2022. 重大滑坡启滑的物理机制是什么?地球科学, 47(10): 3902-3903. doi: 10.3799/dqkx.2022.857

邢珂, 黎昊, 张乐乐, 等, 2025a. 2025年西藏定日Ms6.8地震地表破裂与震害特征分析. 安全与环境工程, 32(2): 20-30.

邢珂, 窦杰, 陈能成, 等, 2025b. 基于光学-LiDAR协同遥感及三维精细建模的滑坡特征解译与稳定性评价. 自然灾害学报, 34(3): 119-132.

许强, 董秀军, 朱星, 等, 2023. 基于实景三维的天-空-地-内滑坡协同观测. 工程地质学报, 31(3): 706-717.

殷跃平, 高少华, 2024. 高位远程地质灾害研究: 回顾与展望. 中国地质灾害与防治学报, 35(1): 1-18.

张世殊, 李青春, 黎昊, 等, 2025. 融合多源遥感数据和改进后Mask R-CNN深度学习模型的复杂高原地形区冰湖智能识别. 地球科学, 50(8): 3132-3143. doi: 10.3799/dqkx.2025.041

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Lightweight Deep Learning for Cross-Scene Landslide Intelligent Recognition with Multi-Source Feature Fusion

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