首页|Exploring deep learning for landslide mapping:A comprehensive review

Exploring deep learning for landslide mapping:A comprehensive review

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A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in high-resolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly data-driven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This 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 analysis.The 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 generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Landslide MappingQuantitative hazard assessmentDeep learningArtificial intelligenceNeural networkBig dataGeological hazard survery engineering

Zhi-qiang Yang、Wen-wen Qi、Chong Xu、Xiao-yi Shao

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College of Surveying and Geo-Informatics,Tongji University,Shanghai 200092,China

National Institute of Natural Hazards,Ministry of Emergency Management,Beijing 100085,China

Key Laboratory of Compound and Chained Natural Hazards Dynamics,Ministry of Emergency Management of China,Beijing 100085,China

National Key Research and Development Program of ChinaNational Institute of Natural Hazards,Ministry of Emergency Management of China

2021YFB39012052023-JBKY-57

2024

中国地质(英文)
中国地质调查局,中国地质科学院

中国地质(英文)

CSTPCD
ISSN:2096-5192
年,卷(期):2024.7(2)
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