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Exploring deep learning for landslide mapping:A comprehensive review

China Geology 2024年第2期7卷 330-350页

作者： Zhi-qiang Yang Wen-wen Qi Chong Xu Xiao-yi Shao College of Surveying and Geo-Informatics Tongji UniversityShanghai 200092China National Institute of Natural hazards Ministry of Emergency ManagementBeijing 100085China Key Laboratory of Compound and Chained Natural hazards Dynamics Ministry of Emergency Management of ChinaBeijing 100085China

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land *** methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective *** advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous *** this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were *** study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard *** results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and ***,we explored the hindrances of DL in landslide hazard research based on the above research *** such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

关键词： Landslide Mapping Quantitative hazard assessment Deep learning Artificial intelligence Neural network Big data geological hazard survery engineering

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