TU3.R6.1
IMPROVING OPERATIONAL SAR-BASED FLOOD INUNDATION MAPPING THROUGH PHYSICALLY-INFORMED POST-PROCESSING OF MACHINE LEARNING PREDICTIONS
Yun-Jae Choung, Steven Burian, The University of Alabama, United States; David Vallee, NOAA National Water Center, United States
Session:
TU3.R6: Harnessing Geospatial Technology and Machine Learning for Flood Management in Fluvial and Glacierized Catchments Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Tue, 11 Aug, 13:45 - 14:00
Presentation
Discussion
Resources
No resources available.
Session TU3.R6
TU3.R6.1: IMPROVING OPERATIONAL SAR-BASED FLOOD INUNDATION MAPPING THROUGH PHYSICALLY-INFORMED POST-PROCESSING OF MACHINE LEARNING PREDICTIONS
Yun-Jae Choung, Steven Burian, The University of Alabama, United States; David Vallee, NOAA National Water Center, United States
TU3.R6.2: SENTINEL-1 SAR–BASED MAPPING OF THE 2024 EXTREME FLOOD EVENT IN THE PORTO ALEGRE METROPOLITAN REGION AND SURROUNDING AREAS, BRAZIL
Tania Hoffmann, National Institute for Space Research, Brazil; Paulo Silva, Eliezer Flores, Aeronautics Institute of Technology, Brazil; Andre Garcia, National Institute for Space Research, Brazil; Dimas Alves, Aeronautics Institute of Technology, Brazil; Angelica Giarolla, Marcos Adami, National Institute for Space Research, Brazil
TU3.R6.3: FLOOD-INVONET: MISH-INVOLUTION ASSISTED MULTI DILATION MULTI KERNEL IMPROVED U-NET FOR FLOOD MAPPING
Kavita Bathe, K J Somaiya Institute of Technology, India; S V Shiva Prasad Sharma, National Remote Sensing Center, Indian Space Research Organization, India; Vimal Mehta, Devanand Bathe, K J Somaiya Institute of Technology, India
TU3.R6.5: SAR SCATTERING MECHANISM SENSITIVE INDICES: AN APPROACH BEYOND BINARY FLOOD EXTENT MAP IN HETEROGENEOUS LANDSCAPES
Rajeev Ranjan, Ashok K. Keshari, Indian Institute of Technology Delhi, India; Erika Podest, NASA, California Institute of Technology, United States
Contacts