A novel landslide susceptibility prediction framework based on contrastive loss

Ouyang, Shubing; Chen, Weitao; Liu, Hangyuan; Li, Yuanyao; Xu, Zhanya

doi:10.1080/15481603.2024.2306740

GIScience & Remote Sensing

2024

DOI: 10.1080/15481603.2024.2306740

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A novel landslide susceptibility prediction framework based on contrastive loss

Shubing Ouyang,

Weitao Chen,

Hangyuan Liu

et al.

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2024

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Cited by 3 publications

References 57 publications

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A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types

Yu,

Pradhan,

Wang

2024

Geoscience Frontiers

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A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types

Yu,

Pradhan,

Wang

2024

Geoscience Frontiers

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Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

Zhao,

Wang,

Chen

et al. 2024

GIScience & Remote Sensing

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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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A novel landslide susceptibility prediction framework based on contrastive loss

Cited by 3 publications

References 57 publications

A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types

A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

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

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