Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning

Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. Accurate and robust flood detection including delineating open water flood areas and identifying flood levels can aid in disaster response and mitigation. However, estimating flood levels remotely is of essence as physical access to flooded areas is limited and the ability to deploy instruments in potential flood zones can be dangerous. Aligning flood extent mapping with local topography can provide a plan-of-action that the disaster response team can consider. Thus, remote flood level estimation via satellites like Sentinel-1 can prove to be remedial. The Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, (2) generating pseudo labels or low confidence labels on the unlabeled test dataset, and then, combining the generated labels with the previously available high confidence labeled dataset. This assimilated dataset is used for the next round of training ensemble models. This cyclical process is repeated until the performance improvement plateaus. Additionally, we post process our results with Conditional Random Fields. Our approach sets a high score on the public leaderboard for the ETCI competition with 0.7654 IoU. Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset on GitHub. To the best of our knowledge we believe this the first works to try out semi-supervised learning to improve flood segmentation models.