FUSING DIGITAL ELEVATION MAPS WITH SATELLITE IMAGERY FOR FLOOD MAPPING

Floods are one of the most severe natural catastrophes and therefore emergency response operations are crucial in order to save lifes. These operations require information about flooded areas so that rescue missions can precisely and efficiently use their available resources. This requires a quick automated procedure which is able to identify these regions from remote sensing images. To achieve this goal we utilize machine learning and apply our method on the Sen1Floods11 dataset. Our main contribution lies in the fusion of Digital Elevation Maps (DEMs) with Satellite data. We investigate the effect of several different combinations of processing methods of DEMs, such as depression filling, deriving slope and curvature or flow metrics. In total 44 different experiments have been performed where our best performing combination outperformed the benchmark in terms of mean IoU. Lastly, we also publish our code for downloading and processing DEMS as well as running our experiments.