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Volume 35 Issue 1
Feb 2024
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Journal of Earth Science  > 2024  >  35(1): 296-300.
Pengfei Feng, Changdong Li, Shuang Zhang, Jie Meng, Jingjing Long. Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection. Journal of Earth Science, 2024, 35(1): 296-300. doi: 10.1007/s12583-023-1957-5
Citation: Pengfei Feng, Changdong Li, Shuang Zhang, Jie Meng, Jingjing Long. Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection. Journal of Earth Science, 2024, 35(1): 296-300. doi: 10.1007/s12583-023-1957-5
shu

Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection

doi: 10.1007/s12583-023-1957-5
  • 1.

    School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China

  • 2.

    Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

  • 3.

    Department of Civil Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China

  • 4.

    College of Geology Engineering and Geomatics, Chang' an University, Xi' an 710054, China

  • 5.

    Department of Geosciences, University of Oslo, Oslo 0316, Norway

More Information
  • Corresponding author: Changdong Li, lichangdong@cug.edu.cn
  • Received Date: 13 Oct 2023
  • Accepted Date: 01 Dec 2023
    • Available Online: 01 Mar 2024
  • Issue Publish Date: 29 Feb 2024
    Abstract
    • Conflict of Interest
    The authors declare that they have no conflict of interest.
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    • References(23)
  • Casagli, N., Intrieri, E., Tofani, V., et al., 2023. Landslide Detection, Monitoring and Prediction with Remote-Sensing Techniques. Nature Reviews Earth & Environment, 4(1): 51–64. https://doi.org/10.1038/s43017-022-00373-x
    Cui, P., Peng, J. B., Shi, P. J., et al., 2021. Scientific Challenges of Research on Natural Hazards and Disaster Risk. Geography and Sustainability, 2(3): 216–223. https://doi.org/10.1016/j.geosus.2021.09.001
    Dai, C., Li, W. L., Wang, D., et al., 2021. Active Landslide Detection Based on Sentinel-1 Data and InSAR Technology in Zhouqu County, Gansu Province, Northwest China. Journal of Earth Science, 32(5): 1092–1103. https://doi.org/10.1007/s12583-020-1380-0
    Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., et al., 2019. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing, 11(2): 196–216. https://doi.org/10.3390/rs11020196
    Guo, C., Xu, Q., Dong, X. J., et al., 2021. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. Journal of Earth Science, 32(5): 1079–1091. https://doi.org/10.1007/s12583-021-1467-2
    Guo, J., Xu, M., Zhang, Q., et al., 2020. Reservoir Regulation for Control of an Ancient Landslide Reactivated by Water Level Fluctuations in Heishui River, China. Journal of Earth Science, 31(6): 1058–1067. https://doi.org/10.1007/s12583-020-1341-7
    He, K. M., Zhang, X. Y., Ren, S. Q., et al., 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27–30, 2016, Las Vegas, NV, USA. IEEE: 770–778. https://doi.org/10.1109/CVPR.2016.90
    Huang, G., Liu, Z., Van Der Maaten, L., et al., 2017. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21–26, 2017, Honolulu, HI, USA. IEEE: 2261–2269. https://doi.org/10.1109/CVPR.2017.243
    Iandola, F. N., Han, S., Moskewicz, M. W., et al., 2016. SqueezeNet: AlexNet-Level Accuracy with 50x fewer Parameters and < 0.5 MB Model Size. arXiv: 1602.07360. https://doi.org/10.48550/arXiv.1602.07360
    Ji, S. P., Yu, D. W., Shen, C. Y., et al., 2020. Landslide Detection from an Open Satellite Imagery and Digital Elevation Model Dataset Using Attention Boosted Convolutional Neural Networks. Landslides, 17(6): 1337–1352. https://doi.org/10.1007/s10346-020-01353-2
    Kawabata, D., Bandibas, J., 2009. Landslide Susceptibility Mapping Using Geological Data, a DEM from ASTER Images and an Artificial Neural Network (ANN). Geomorphology, 113(1/2): 97–109. https://doi.org/10.1016/j.geomorph.2009.06.006
    Li, C. D., Criss, R. E., Fu, Z. Y., et al., 2021. Evolution Characteristics and Displacement Forecasting Model of Landslides with Stair-Step Sliding Surface along the Xiangxi River, Three Gorges Reservoir Region, China. Engineering Geology, 283: 105961. https://doi.org/10.1016/j.enggeo.2020.105961
    Li, Y., Wang, P., Feng, Q. L., et al., 2023. Landslide Detection Based on Shipborne Images and Deep Learning Models: A Case Study in the Three Gorges Reservoir Area in China. Landslides, 20(3): 547–558. https://doi.org/10.1007/s10346-022-01997-2
    Li, Z. H., Zhang, C. L., Chen, B., et al., 2022. A Technical Framework of Landslide Prevention Based on Multi-Source Remote Sensing and Its Engineering Application. Earth Science, 47(6): 1901–1916. https://doi.org/10.3799/dqkx.2022.205 (in Chinese with English Abstract)
    Long, J. J., Li, C. D., Liu, Y., et al., 2022. A Multi-Feature Fusion Transfer Learning Method for Displacement Prediction of Rainfall Reservoir-Induced Landslide with Step-Like Deformation Characteristics. Engineering Geology, 297: 106494. https://doi.org/10.1016/j.enggeo.2021.106494
    Meng, J., Li, C. D., Zhou, J. Q., et al., 2023. Multiscale Evolution Mechanism of Sandstone under Wet-Dry Cycles of Deionized Water: From Molecular Scale to Macroscopic Scale. Journal of Rock Mechanics and Geotechnical Engineering, 15(5): 1171–1185. https://doi.org/10.1016/j.jrmge.2022.10.008
    Redmon, J., Farhadi, A., 2018. YOLOv3: An Incremental Improvement. arXiv: 1804.02767. https://doi.org/10.48550/arXiv.1804.02767
    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
    Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv: 1409.1556. https://doi.org/10.48550/arXiv.1409.1556
    Szegedy, C., Liu, W., Jia, Y. Q., et al., 2015. Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 7–12, 2015, Boston, MA, USA. IEEE: 1–9. https://doi.org/10.1109/CVPR.2015.7298594
    Tan, M. X., Le, Q. V., 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv: 1905.11946. https://doi.org/10.48550/arXiv.1905.11946
    Tang, H. M., Yong, R., Ez Eldin, M. A. M., 2017. Stability Analysis of Stratified Rock Slopes with Spatially Variable Strength Parameters: The Case of Qianjiangping Landslide. Bulletin of Engineering Geology and the Environment, 76(3): 839–853. https://doi.org/10.1007/s10064-016-0876-4
    Yan, Y., Guo, C., Zhong, N., et al., 2022. Deformation Characteristics of Jiaju Ancient Landslide Based on InSAR Monitoring Method, Sichuan, China. Earth Science, 47(12): 4681–4697. https://doi.org/10.3799/dqkx.2022.162 (in Chinese with English Abstract)
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