  Accurate flood detection in near real time via high resolution, high latency
satellite imagery is essential to prevent loss of lives by providing quick and
actionable information. Instruments and sensors useful for flood detection are
only available in low resolution, low latency satellites with region re-visit
periods of up to 16 days, making flood alerting systems that use such
satellites unreliable. This work presents H2O-Network, a self supervised deep
learning method to segment floods from satellites and aerial imagery by
bridging domain gap between low and high latency satellite and coarse-to-fine
label refinement. H2O-Net learns to synthesize signals highly correlative with
water presence as a domain adaptation step for semantic segmentation in high
resolution satellite imagery. Our work also proposes a self-supervision
mechanism, which does not require any hand annotation, used during training to
generate high quality ground truth data. We demonstrate that H2O-Net
outperforms the state-of-the-art semantic segmentation methods on satellite
imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of
flood segmentation. We emphasize the generalizability of our model by
transferring model weights trained on satellite imagery to drone imagery, a
highly different sensor and domain.
