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New UFO dataset aids urban flood mapping from satellite imagery

Researchers have introduced Urban Flood Observations (UFO), a new global dataset featuring hand-labeled post-flood inundation maps derived from high-resolution satellite imagery. The dataset contains 215 image chips from 14 flood events between 2017 and 2021, annotated for 'inundated' and 'non-inundated' areas. When used to train a segmentation model, UFO enabled an IoU of 77.3, significantly outperforming existing surface water products like NASA's IMPACT and Google's Dynamic World. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Provides a benchmark dataset to improve AI-driven flood mapping accuracy and validation.

RANK_REASON This is a research paper describing a new dataset for flood inundation mapping.

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Rohit Mukherjee, Hannah K. Friedrich, Beth Tellman, Ariful Islam, Zhijie Zhang, Jonathan Giezendanner, Upmanu Lall, Venkataraman Lakshmi ·

    Urban Flood Observations (UFO): A hand-labeled training and validation dataset of post-flood inundation

    arXiv:2604.23066v1 Announce Type: new Abstract: Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We p…