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RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features

Author

Listed:
  • Bangjie Fu

    (Central South University)

  • Yange Li

    (Central South University)

  • Zheng Han

    (Central South University
    Hunan Provincial Key Laboratory for Disaster Prevention and Mitigation of Rail Transit Engineering Structures)

  • Zhenxiong Fang

    (Central South University)

  • Ningsheng Chen

    (Chinese Academy of Sciences)

  • Guisheng Hu

    (Chinese Academy of Sciences)

  • Weidong Wang

    (Central South University)

Abstract

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model.

Suggested Citation

  • Bangjie Fu & Yange Li & Zheng Han & Zhenxiong Fang & Ningsheng Chen & Guisheng Hu & Weidong Wang, 2023. "RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 701-719, October.
    • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06145-0
    DOI: 10.1007/s11069-023-06145-0
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    References listed on IDEAS

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    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
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