Exploring deep learning for landslide mapping: A comprehensive review Article Swipe
Zhiqiang Yang
,
Wenwen Qi
,
Chong Xu
,
Xiaoyi Shao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31035/cg2024032
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31035/cg2024032
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.31035/cg2024032
- OA Status
- diamond
- Cited By
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402612913
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402612913Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31035/cg2024032Digital Object Identifier
- Title
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Exploring deep learning for landslide mapping: A comprehensive reviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Zhiqiang Yang, Wenwen Qi, Chong Xu, Xiaoyi ShaoList of authors in order
- Landing page
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https://doi.org/10.31035/cg2024032Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.31035/cg2024032Direct OA link when available
- Concepts
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Landslide, Geology, Cartography, Geography, Remote sensing, Artificial intelligence, Computer science, SeismologyTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 4Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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