Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Chen, Tzu-Hsin Karen; Kincey, Mark E.; Rosser, Nick J.; Seto, Karen C.

doi:10.1016/j.scitotenv.2024.171161

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2024.171161

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Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Tzu-Hsin Karen Chen,

Mark E. Kincey,

Nick J. Rosser

et al.

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Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Akosah,

Gratchev,

Kim

et al. 2024

Remote Sensing

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This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed.

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Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Akosah,

Gratchev,

Kim

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Cited by 1 publication

References 55 publications

Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

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