EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep learning, remote sensing and visual analytics to support automatic flood detection

Binayak Ghosh1,4, Shagun Garg2, Mahdi Motagh1, Daniel Eggert1, Mike Sips1, Sandro Martinis5, and Simon Plank5
Binayak Ghosh et al.
  • 1Remote Sensing and Geoinformatics, GFZ German Research Centre for Geosciences, Potsdam, Germany, 14473
  • 2Future Infrastructure and Built Environment (FIBE), Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
  • 4Department of Computer Science, Technical University of Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
  • 5German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Münchener Straße 20, 82234 Oberpfaffenhofen, Germany

Floods can have devastating consequences on people, infrastructure, and the ecosystem. Satellite imagery has proven to be an efficient instrument in supporting disaster management authorities during flood events. In contrast to optical remote sensing technology, Synthetic Aperture Radar (SAR) can penetrate clouds, and authorities can use SAR images even during cloudy circumstances. A challenge with SAR is the accurate classification and segmentation of flooded areas from SAR imagery. Recent advancements in deep learning algorithms have demonstrated the potential of deep learning for image segmentation demonstrated. Our research adopted deep learning algorithms to classify and segment flooded areas in SAR imagery. We used UNet and Feature Pyramid Network (FPN), both based on EfficientNet-B7 implementation, to detect flooded areas in SAR imaginary of Nebraska, North Alabama, Bangladesh, Red River North, and Florence. We evaluated both deep learning methods' predictive accuracy and will present the evaluation results at the conference. In the next step of our research, we develop an XAI toolbox to support the interpretation of detected flooded areas and algorithmic decisions of the deep learning methods through interactive visualizations.

How to cite: Ghosh, B., Garg, S., Motagh, M., Eggert, D., Sips, M., Martinis, S., and Plank, S.: Deep learning, remote sensing and visual analytics to support automatic flood detection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12271,, 2022.