Automatic Collection of Training Samples for Flooded Areas

We show the application of an automatic collection of training samples for the identification of flooded buildings. The method is based on a near real time estimation of the flooded area using in-place sensors and a numerical simulation. Then, microwave remote sensing images are used to improve the accuracy of the extent of the flooded area. The floods produced during the 2018 heavy rainfalls in the town of Mabi is reported as case study. The results are consistent with the flood map provided by a third party.