Machine learning-driven integration of time-series InSAR and multiple surface factors for landslide identification and susceptibility assessment

Machine learning-driven integration of time-series InSAR and multiple surface factors for landslide identification and susceptibility assessment

Landslides pose a significant threat to the safety of reservoirs, particularly those situated in canyon terrains. This study aims to enhance the safety and security of reservoir areas by proposing an integrated method for the automatic identification and assessment of landslides. By combining the SB...

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Bibliographic Details
Main Authors: Qianyu Wang, Wen Zhang, Jinglin Li, Ziyang Li, Zhi Luo, Qiang Zhao, Jianbo Jian, Fang Shangguan, Yuanxing Yang, Yangyang Ma, Zhen Zhang, Shuangming Zhao, Linyi Li, Lingkui Meng
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
Subjects:
SBAS-InSAR
automatic landslide identification
machine learning
landslide susceptibility assessment
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2543561
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