Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection

Pengfei Feng , Changdong Li , Shuang Zhang , Jie Meng , Jingjing Long

Journal of Earth Science ›› 2024, Vol. 35 ›› Issue (1) : 296 -300.

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Journal of Earth Science ›› 2024, Vol. 35 ›› Issue (1) : 296 -300. DOI: 10.1007/s12583-023-1957-5
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Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection

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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

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References

[1]

Casagli N, Intrieri E, Tofani V, . Landslide Detection, Monitoring and Prediction with Remote-Sensing Techniques. Nature Reviews Earth & Environment, 2023, 4(1): 51-64.

[2]

Cui P, Peng J B, Shi P J, . Scientific Challenges of Research on Natural Hazards and Disaster Risk. Geography and Sustainability, 2021, 2(3): 216-223.

[3]

Dai C, Li W L, Wang D, . Active Landslide Detection Based on Sentinel-1 Data and InSAR Technology in Zhouqu County, Gansu Province, Northwest China. Journal of Earth Science, 2021, 32(5): 1092-1103.

[4]

Ghorbanzadeh O, Blaschke T, Gholamnia K, . Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing, 2019, 11(2): 196-216.

[5]

Guo C, Xu Q, Dong X J, . Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. Journal of Earth Science, 2021, 32(5): 1079-1091.

[6]

Guo J, Xu M, Zhang Q, . Reservoir Regulation for Control of an Ancient Landslide Reactivated by Water Level Fluctuations in Heishui River, China. Journal of Earth Science, 2020, 31(6): 1058-1067.

[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

[8]

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

[9]

Iandola, F. N., Han, S., Moskewicz, M. W., et al., 2016. SqueezeNet: AlexNetLevel Accuracy with 50x fewer Parameters and < 0.5 MB Model Size. arXiv: 1602.07360. https://doi.org/10.48550/arXiv.1602.07360

[10]

Ji S P, Yu D W, Shen C Y, . Landslide Detection from an Open Satellite Imagery and Digital Elevation Model Dataset Using Attention Boosted Convolutional Neural Networks. Landslides, 2020, 17(6): 1337-1352.

[11]

Kawabata D, Bandibas J. Landslide Susceptibility Mapping Using Geological Data, a DEM from ASTER Images and an Artificial Neural Network (ANN). Geomorphology, 2009, 113(1/2): 97-109.

[12]

Li C D, Criss R E, Fu Z Y, . Evolution Characteristics and Displacement Forecasting Model of Landslides with Stair-Step Sliding Surface along the Xiangxi River, Three Gorges Reservoir Region, China. Engineering Geology, 2021, 283 105961

[13]

Li Y, Wang P, Feng Q L, . Landslide Detection Based on Shipborne Images and Deep Learning Models: A Case Study in the Three Gorges Reservoir Area in China. Landslides, 2023, 20(3): 547-558.

[14]

Li Z H, Zhang C L, Chen B, . A Technical Framework of Landslide Prevention Based on Multi-Source Remote Sensing and Its Engineering Application. Earth Science, 2022, 47(6): 1901-1916. (in Chinese with English Abstract)

[15]

Long J J, Li C D, Liu Y, . A Multi-Feature Fusion Transfer Learning Method for Displacement Prediction of Rainfall Reservoir-Induced Landslide with Step-Like Deformation Characteristics. Engineering Geology, 2022, 297 106494

[16]

Meng J, Li C D, Zhou J Q, . 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, 2023, 15(5): 1171-1185.

[17]

Redmon, J., Farhadi, A., 2018. YOLOv3: An Incremental Improvement. arXiv: 1804.02767. https://doi.org/10.48550/arXiv.1804.02767

[18]

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

[19]

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

[20]

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

[21]

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

[22]

Tang H M, Yong R, Ez Eldin M A M. Stability Analysis of Stratified Rock Slopes with Spatially Variable Strength Parameters: The Case of Qianjiangping Landslide. Bulletin of Engineering Geology and the Environment, 2017, 76(3): 839-853.

[23]

Yan Y, Guo C, Zhong N, . Deformation Characteristics of Jiaju Ancient Landslide Based on InSAR Monitoring Method, Sichuan, China. Earth Science, 2022, 47(12): 4681-4697. (in Chinese with English Abstract)

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