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Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area

Author

Listed:
  • Fan Yang

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

  • Xiaozhi Men

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

  • Yangsheng Liu

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

  • Huigeng Mao

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

  • Yingnan Wang

    (No.8 Institute of Geology and Mineral Resources Exploration of Shandong Province, Rizhao 276826, China)

  • Li Wang

    (No.1 Institute of Geology and Mineral Resource Exploration of Shandong Province, Jinan 250010, China)

  • Xiran Zhou

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

  • Chong Niu

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

  • Xiao Xie

    (Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China)

Abstract

Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility prediction with multi-modal remote sensing data involving digital elevation models, optical remote sensing, and an SAR dataset. Moreover, based on the results generated by multi-modal remote sensing data, we further conducted landslide and mudslide susceptibility prediction with semantic knowledge. Through the comparisons with the ground truth datasets created by field investigation, experimental results have proved that remote sensing data can only enhance deep learning techniques to detect the landslide and mudslide, rather than the landslide and mudslide susceptibility. Knowledge regarding the potential clues about landslide and mudslide, which would be critical for estimating landslide and mudslide susceptibility, have not been comprehensively investigated yet.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1949-:d:1264227
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    References listed on IDEAS

    as
    1. 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.
    2. Israr Ullah & Bilal Aslam & Syed Hassan Iqbal Ahmad Shah & Aqil Tariq & Shujing Qin & Muhammad Majeed & Hans-Balder Havenith, 2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping," Land, MDPI, vol. 11(8), pages 1-20, August.
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    Cited by:

    1. Shaohan Zhang & Shucheng Tan & Yongqi Sun & Duanyu Ding & Wei Yang, 2024. "Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)," Land, MDPI, vol. 13(9), pages 1-25, August.

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