remote sensing Article Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery Edoardo Nemni 1,* , Joseph Bullock 2,3 , Samir Belabbes 1 and Lars Bromley 1 1 United Nations Institute for Training and Research’s (UNITAR) Operational Satellite Applications Programme (UNOSAT), CERN, 1211 Meyrin, Switzerland;
[email protected] (S.B.);
[email protected] (L.B.) 2 United Nations Global Pulse, New York, NY 10017, USA;
[email protected] 3 Institute for Data Science, Durham University, Durham DH1 3LE, UK * Correspondence:
[email protected] Received: 1 June 2020; Accepted: 30 July 2020; Published: 6 August 2020 Abstract: Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection.