# H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement Source: [H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:38:00.142Z Authors: Peri Akiva, Matthew Purri, Kristin Dana, Beth Tellman, Tyler Anderson Published: 2020-10-11T18:35:03.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Hand Mesh Reconstruction on DexYCB (S0)](): Hand Mesh Reconstruction; DexYCB (S0); PA-V-PE: 5.5. ## Original sources and implementation [Original paper]() [PDF]()