H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement

@article{Akiva2021H2ONetSF,
  title={H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement},
  author={Peri Akiva and Matthew Purri and Kristin J. Dana and Beth Tellman and Tyler Anderson},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={111-122}
}
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… 

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