A Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture Radar

Tiampo, K.Woods, C.Huang, L.Sharma, P.Chen, Z.Kar, B.Bausch, D.Simmons, C.Estrada, R.Willis, Michael J.Glasscoe, M.2024-02-212024-02-212021-01-019781665403696https://hdl.handle.net/10919/118097The rising number of flooding events combined with increased urbanization is contributing to significant economic losses due to damages to structures and infrastructures. Here we present a method for producing all weather maps of flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods that can be used to provide information on the evolution of flood hazards to DisasterAware©, a global alerting system, that is used to disseminate flood risk information to stakeholders across the globe. While these efforts are still in development, a case study is presented for the major flood event associated with Hurricane Harvey and associated floods that impacted Houston, TX in August of 2017.Pages 558-561application/pdfenPublic Domain (U.S.)Synthetic aperture radarFlood characterizationFlood inundationMachine learningGeospatial data fusionA Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture RadarConference proceedingInternational Geoscience and Remote Sensing Symposium (IGARSS)https://doi.org/10.1109/IGARSS47720.2021.95536012021-July