Flooded and non-flooded regions were delineated from recent Hurricanes using a random forest classification model leveraging pre- and post-storm synthetic aperture radar from the European Space Agency's Sentinel-1 sensor, in addition to topography, floodplain, and landcover data. The classification model was trained with USGS and NCDEMS high-water marks, in addition to flooded and non-flooded regions delineated from high-resolution NOAA aerial photography; the model achieved >91% accuracy against an independent withheld samplefor each storm. Within regions where flooding was detected, opportunities for buyouts, conservation of forests and wetlands, or lands where restoration or easements could be implemented were identified using the National Land Cover Dataset (2011). For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below.

Flooded and non-flooded regions were delineated from recent Hurricanes using a random forest classification model leveraging pre- and post-storm synthetic aperture radar from the European Space Agency's Sentinel-1 sensor, in addition to topography, floodplain, and landcover data. The classification model was trained with USGS and NCDEMS high-water marks, in addition to flooded and non-flooded regions delineated from high-resolution NOAA aerial photography; the model achieved >91% accuracy against an independent withheld samplefor each storm. Within regions where flooding was detected, opportunities for buyouts, conservation of forests and wetlands, or lands where restoration or easements could be implemented were identified using the National Land Cover Dataset (2011). For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below.