Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area
Author:
Yang Fan1, Men Xiaozhi1, Liu Yangsheng1, Mao Huigeng1, Wang Yingnan2, Wang Li3, Zhou Xiran4ORCID, Niu Chong1, Xie Xiao5ORCID
Affiliation:
1. Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China 2. No.8 Institute of Geology and Mineral Resources Exploration of Shandong Province, Rizhao 276826, China 3. No.1 Institute of Geology and Mineral Resource Exploration of Shandong Province, Jinan 250010, China 4. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China 5. 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.
Funder
Fundamental Applied Research Foundation of Liaoning Province Key Technology Research and Development Program of Shandong Provincial Bureau of Geology & Mineral Resources Outstanding Young Scholars of SDGM Shenyang Young and Middle-aged Scientific and Technological Talents Program Weifang Science and Technology Project
Subject
Nature and Landscape Conservation,Ecology,Global and Planetary Change
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