Research on the assessment of landslide risk on highways based on remote sensing data and Xgboost algorithm

Jianming Zhang; Ying Han; Yuze Zhang; Lei Deng; Yunhua Sun; Yu Zang; Wencan Jiao; Minlu Zhou

doi:10.1117/12.3019385

23 January 2024 Research on the assessment of landslide risk on highways based on remote sensing data and Xgboost algorithm

Jianming Zhang, Ying Han, Yuze Zhang, Lei Deng, Yunhua Sun, Yu Zang, Wencan Jiao, Minlu Zhou

Author Affiliations +

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129781Z (2024) https://doi.org/10.1117/12.3019385
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Highway landslides are a natural disaster that can have devastating impacts on transportation, the economy, society, and people's lives. To mitigate the damage caused by these landslides, it is crucial to assess their susceptibility. In this study, we propose a novel method for assessing the risk of highway landslides using an XGBoost model that utilizes a variety of data sources, including remote sensing data, terrain, and meteorology. The results of our research are highly promising. Our model achieved an AUC score greater than 0.93/0.83 on the test/validation set, indicating high accuracy in predicting the susceptibility of landslides. By identifying areas that are at high risk for landslides, we can take proactive steps to prevent or mitigate the damage caused by these natural disasters. The insights gained from our study have important implications for land use planning, infrastructure development, and emergency management decision-making.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianming Zhang, Ying Han, Yuze Zhang, Lei Deng, Yunhua Sun, Yu Zang, Wencan Jiao, and Minlu Zhou "Research on the assessment of landslide risk on highways based on remote sensing data and Xgboost algorithm", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129781Z (23 January 2024); https://doi.org/10.1117/12.3019385

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