Satellite-based information for mapping flood extent is not always available. Sometimes due to satellite orbits and revisit time or due to clouds in the case of optical satellites.
The intention of this research was to develop a two-step ML-based automatic workflow that would first detect the flood from radar (Sentinel 1) or multispectral (Sentinel 2) satellite. Then in the second step to model the possible flood extent (under the clouds or between the revisit time) from the detected flood extent with the help of the Random Forest algorithm and the variables extracted from the Digital Elevation Model.
Preliminary results confirm the potential to detect the extent of the existing flood under the clouds or to predict the possible maximum extent of the downstream flood in the upcoming days.
These results can greatly shorten the time for flood/water management-related actions, especially in situations when every minute can mean the difference between life and death.
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