MOP2.PL.4
HYBRID DEEP LEARNING METHOD FOR LANDSLIDE SUSCEPTIBILITY MAPPING
Liu Yang, Jiao Wu, Jiangxi University of Water Resources and Electric Power, China
Session:
MOP2.PL: Geology and Geomorphology II Poster
Track:
Land Applications
Location:
TBD
Presentation Time:
Mon, 10 Aug, 15:00 - 16:15
Presentation
Discussion
Resources
No resources available.
Session MOP2.PL
MOP2.PL.1: EVALUATION AND SYSTEM CONSTRUCTION OF GEOLOGICAL HAZARD RISK WARNING BASED ON METEOROLOGICAL FACTORS IN CHONGQING
Fengmin Wu, Zhipeng Zheng, Chao Yuan, Chongqing Geomatics and Remote Sensing Center, China; Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, China, China; Jun Jiang, Chongqing Institute of Geological Environment Monitoring, China, China; Lin Li, Yan Hu, Jieqi Yuan, Bin Zhang, Chongqing Geomatics and Remote Sensing Center, China; Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, China, China
MOP2.PL.2: RAINFALL-DRIVEN SPATIOTEMPORAL MODELING FOR LANDSLIDE HAZARD ASSESSMENT USING DEEP LEARNING
Jiao Wu, Liu Yang, Jiangxi University of Water Resources and Electric Power, China
MOP2.PL.3: HODS - High-resolution Optical Deformation Service for tracking landslide terrain motion from optical image time series
Wirtz Bastien, Jean-Philippe Malet, Floriane Provost, David Michéa, CNRS, France
MOP2.PL.4: HYBRID DEEP LEARNING METHOD FOR LANDSLIDE SUSCEPTIBILITY MAPPING
Liu Yang, Jiao Wu, Jiangxi University of Water Resources and Electric Power, China
Contacts