LSI-YOLOv8: An Improved Rapid and High Accuracy Landslide Identification Model Based on YOLOv8 From Remote Sensing Images
Author:
Affiliation:
1. School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, China
2. Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha, Hunan, China
Funder
Open Fund of Hunan Geological Disaster Monitoring, Early Warning and Emergency Rescue Engineering Technology Research Center
Chinese National College Student Innovation and Entrepreneurship Training Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10591795.pdf?arnumber=10591795
Reference54 articles.
1. Review on remote sensing methods for landslide detection using machine and deep learning
2. GNSS techniques for real-time monitoring of landslides: a review
3. Research on loess landslide identification, monitoring and failure mode with InSAR technique in heifangtai, Gansu;Zhao;Geomatics Inf. Sci. Wuhan Univ.,2019
4. Application of satellite SAR interferometry for the detection and monitoring of landslides along the Tijuana - Ensenada Scenic Highway, Baja California, Mexico
5. Recent progress in landslide monitoring with InSAR;Jianjun;Acta Geodaetica et Cartographica Sinica,2022
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