WE4.R15.4

MONITORING RETROGRESSIVE THAW SLUMPS IN PERMAFROST REGIONS WITH DEEP LEARNING

Konrad Heidler, Technische Universität München, Germany; Ingmar Nitze, Guido Grosse, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany; Xiao Xiang Zhu, Technische Universität München, Germany
Session:
WE4.R15: Machine Learning and Remote Sensing Data for Rapid Disaster Response II Oral
Track:
Community-Contributed Sessions
Location:
Room F
Presentation Time:
Wed, 19 Jul, 16:21 - 16:33 Pacific Time (UTC -8)
Session Co-Chairs:
Marc Wieland, German Aerospace Center (DLR) and Nina Merkle, German Aerospace Center (DLR)
Session Manager:
Ge Jiang
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Session WE4.R15
WE4.R15.1: STATE-OF-THE-ART EARTH OBSERVATION SOLUTIONS SERVING THE OPERATIONAL NEEDS OF DISASTER MANAGEMENT AGENCIES AND CIVIL PROTECTION AUTHORITIES
Charalampos (Haris) Kontoes, Alexia Tsouni, Stella Girtsou, Alexis Apostolakis, Stavroula Alatza, Vassilis Sitokonstantinou, Nikolaos S. Bartsotas, National Observatory of Athens (NOA), Greece
WE4.R15.2: DEVELOPING A FRAMEWORK FOR RAPID COLLAPSED BUILDING MAPPING USING SATELLITE IMAGERY AND DEEP LEARNING MODELS
Bruno Adriano, Tohoku University, Japan; Hiroyuki Miura, Hiroshima University, Japan; Wen Liu, Chiba University, Japan; Masashi Matsuoka, Tokyo Institute of Technology, Japan; Shunichi Koshimura, Tohoku University, Japan
WE4.R15.3: MACHINE LEARNING ALGORITHM COMPARISON FOR AUTOMATIC BURNT AREA MAPPING WITH HIGH-RESOLUTION SATELLITE DATA
Maria Sdraka, Alkinoos Dimakis, Ioannis Papoutsis, Orion Lab, Greece; Zisoula Ntasiou, Alexandros Malounis, Hellenic Fire Service, Greece
WE4.R15.4: MONITORING RETROGRESSIVE THAW SLUMPS IN PERMAFROST REGIONS WITH DEEP LEARNING
Konrad Heidler, Technische Universität München, Germany; Ingmar Nitze, Guido Grosse, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany; Xiao Xiang Zhu, Technische Universität München, Germany
WE4.R15.5: Normalizing Flow-based Deep Variational Bayesian Network for Seismic Multi-hazards and Impacts Estimation from InSAR Imagery
Xuechun Li, Stony Brook University, United States; Paula Burgi, U.S. Geological Survey, United States; Wei Ma, Hong Kong Poly University, Hong Kong SAR China; Hae Young Noh, Stanford University, United States; David Wald, U.S. Geological Survey, United States; Susu Xu, Stony Brook University, United States
Resources
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