H2O-Net: Self-supervised flood segmentation via adversarial domain adaptation and label refinement

Peri Akiva, Matthew Purri, Kristin Dana, Beth Tellman, Tyler Anderson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages111-122
Number of pages12
ISBN (Electronic)9780738142661
DOIs
StatePublished - Jan 2021
Externally publishedYes
Event2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 - Virtual, Online, United States
Duration: Jan 5 2021Jan 9 2021

Publication series

NameProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

Conference

Conference2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Country/TerritoryUnited States
CityVirtual, Online
Period1/5/211/9/21

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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