MO2.R15.2: AUTOMATIC FLOOD DETECTION AND INFORMATION PROVISION USING ALOS-2
Masato Ohki, Yuki Takakura, Shiro Kawakita, Takeo Tadono, Japan Aerospace Exploration Agency, Japan
MO2.R15.3: EFFECT OF TERRAIN INFORMATION ON MULTIMODAL DEEP LEARNING FOR FLOOD DISASTER DETECTION
Takashi Miyamoto, University of Yamanashi, Japan; Marco Stricker, German Research Center for Artificial Intelligence, Germany; Jun Ogishima, University of Yamanashi, Japan; Kevin Iselborn, University of Kaiserslautern-Landau, Germany; Marlon Nuske, Andreas Dengel, German Research Center for Artificial Intelligence, Germany
MO2.R15.4: A COMPARISON OF REMOTE SENSING APPROACHES TO ASSESS THE DEVASTATING MAY-JUNE 2022 FLOODING IN SYLHET, BANGLADESH
Alex Saunders, Jonathan Giezendanner, Beth Tellman, Ariful Islam, University of Arizona, United States; Arifuzzaman Bhuyan, Bangladesh Water Development Board, Bangladesh; A.K.M. Islam, Bangladesh University of Engineering and Technology, Bangladesh
MO2.R15.5: EARLY WARNING FOR ALL WITH A MODEL-OF-MODELS APPROACH
Guy Schumann, ImageCat Inc., United States; Bandana Kar, U.S. Department of Energy, United States; Prativa Sharma, University of Missouri, United States; Doug Bausch, Niyam IT Inc., United States; Jun Wang, Indiana University, United States; Margaret Glasscoe, University of Alabama in Huntsville, United States