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Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters

Author

Listed:
  • Chong Niu

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China)

  • Wenping Yin

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Xue

    (Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China)

  • Yujing Sui

    (Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China)

  • Xingqing Xun

    (Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China)

  • Xiran Zhou

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Sheng Zhang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yong Xue

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Identification of potential landslide hazards is of great significance for disaster prevention and control. CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks) and many other deep learning methods have been used to identify landslide hazards. However, most samples are made with a fixed window size, which affects recognition accuracy to some extent. This paper presents a multi-window hidden danger identification CNN method according to the scale of the landslide in the experimental area. Firstly, the hidden danger area is preliminarily screened by InSAR deformation processing technology. Secondly, based on topography, geology, hydrology and human activities, a total of 15 disaster-prone factors are used to create factor datasets for in-depth learning. According to the general scale of the landslide, models with four window sizes of 48 × 48, 32 × 32, 16 × 16 and 8 × 8 are trained, respectively, and several window models with better recognition effect and suitable for the scale of landslide in the experimental area are selected for the accurate identification of landslide hazards. The results show that, among the four windows, 16 × 16 and 8 × 8 windows have the best model recognition effect. Then, according to the scale of the landslide, these optimal windows are pertinently selected, and the precision, recall rate and F-measure of the multi-window deep learning model are improved (82.86%, 78.75%, 80.75%). The research results prove that the multi-window identification method of landslide hazards combining InSAR technology and factors predisposing to disasters is effective, which can play an important role in regional disaster identification and enhance the scientific and technological support ability of geological disaster prevention and mitigation.

Suggested Citation

  • Chong Niu & Wenping Yin & Wei Xue & Yujing Sui & Xingqing Xun & Xiran Zhou & Sheng Zhang & Yong Xue, 2023. "Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters," Land, MDPI, vol. 12(1), pages 1-15, January.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:173-:d:1025850
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    References listed on IDEAS

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    1. Yifei Zhu & Xin Yao & Leihua Yao & Chuangchuang Yao, 2022. "Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China," 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. 111(1), pages 973-994, March.
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    Cited by:

    1. Fan Yang & Xiaozhi Men & Yangsheng Liu & Huigeng Mao & Yingnan Wang & Li Wang & Xiran Zhou & Chong Niu & Xiao Xie, 2023. "Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area," Land, MDPI, vol. 12(10), pages 1-15, October.

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