Quantitative evaluation of flood extent detection using attention U-Net case studies from Eastern South Wales Australia in March 2021 and July 2022.

Remotely sensed data have increasingly been used to improve flood mapping and modelling, providing much of the required information for delineating flood-affected areas and damage assessment. SAR satellite-based solutions have been proven to be among the most effective tools for flood extent detection because of their large spatial coverage, reasonable revisit time, and ability to penetrate through clouds and provide a full view of the Earth's surface regardless of atmospheric or lighting conditions. This research proposes an innovative approach to applying an attention U-Net on SAR datasets to detect and extract flood extent maps. The approach was developed and validated using the datasets collected during a flooding event after extreme rainfall hit the eastern coast of Australia on 18 March 2021. Sentinel-1 (S1) ground range detected (GRD) and single look complex (SLC) descending track of the pre-and post-event on the 12th and 24th of March 2021, have been pre-processed, coincide with labels area of the flood extension have been carefully delineated to feed the model. The attention U-Net approach on S1 cross-polarization of VH provided promising results to identify the flood extent with precision, recall, and F1-score of 0.90, 0.88, 0.89 correspondingly. At the same time the result of the unseen frame achieved precision, recall, and F1-score, of 0.63, 0.59, and 0.61 respectively. The approach was also successfully employed to detect flood extent over the study area in July 2022, and the proposed model gave an outstanding accuracy of over 0.84 F1-score.