WE3.R15.2

COMBINING DEEP LEARNING AND NUMERICAL SIMULATION TO PREDICT FLOOD INUNDATION DEPTH

Bruno Adriano, Tohoku University, Japan; Naoto Yokoya, The University of Tokyo, Japan; Kazuki Yamanoi, Kyoto University, Japan; Satoru Oishi, RIKEN Center for Computational Science, Japan
Session:
WE3.R15: Machine Learning and Remote Sensing Data for Rapid Disaster Response I Oral
Track:
Community-Contributed Sessions
Location:
Room F
Presentation Time:
Wed, 19 Jul, 13:12 - 13:24 Pacific Time (UTC -8)
Session Co-Chairs:
Nina Merkle, German Aerospace Center (DLR) and Marc Wieland, German Aerospace Center (DLR)
Session Manager:
Ge Jiang
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Session WE3.R15
WE3.R15.1: RescueNet-VQA: A Large-Scale Visual Question Answering Benchmark for Damage Assessment
Maryam Rahnemoonfar, Lehigh University, United States; Argho Sarkar, University of Maryland Baltimore County, United States
WE3.R15.2: COMBINING DEEP LEARNING AND NUMERICAL SIMULATION TO PREDICT FLOOD INUNDATION DEPTH
Bruno Adriano, Tohoku University, Japan; Naoto Yokoya, The University of Tokyo, Japan; Kazuki Yamanoi, Kyoto University, Japan; Satoru Oishi, RIKEN Center for Computational Science, Japan
WE3.R15.3: BUILDING DAMAGE IDENTIFICATION IN VARIOUS NATURAL DISASTERS FROM INTERFEROMETRIC SAR COHERENCE AND MACHINE LEARNING
Hiroyuki Miura, Masaya Shimotake, Hiroshima University, Japan
WE3.R15.4: VARIATIONAL AUTOENCODERS FOR UNSUPERVISED OBJECT COUNTING FROM VHR IMAGERY: APPLICATIONS IN DWELLING EXTRACTION FROM FORCIBLY DISPLACED PEOPLE SETTLEMENT AREAS
Getachew Workineh Gella, Paris Lodron University of Salbzurg, Austria; Hugo Gangloff, Université Paris-Saclay, France; Lorenz Wendt, Dirk Tiede, Stefan Lang, Paris Lodron University of Salzburg, Austria
WE3.R15.5: AD-HOC SITUATIONAL AWARENESS DURING FLOODS USING REMOTE SENSING DATA AND MACHINE LEARNING METHODS
Marc Wieland, Nina Merkle, Anne Schneibel, Corentin Henry, Konstanze Lechner, Xiangtian Yuan, Seyed Majid Azimi, Veronika Gstaiger, Sandro Martinis, German Aerospace Center (DLR), Germany
Resources
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