CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12978 > Article
Paper
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
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Network landslides
Remote sensing
Education and training
Machine learning
Statistical modeling
Performance modeling
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top