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

arXiv: https://arxiv.org/abs/2604.23066 · HTML: https://mlsanity.com/paper/2604.23066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping](https://mlsanity.com/paper/2406.04111/) (2024)
- [Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning](https://mlsanity.com/paper/2107.08369/) (2021)
- [Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping](https://mlsanity.com/paper/2305.00640/) (2023)

## Abstract (arXiv)

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 present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-existing water bodies (permanent or seasonal)) and 'non-inundated'. To demonstrate the dataset's utility, we trained a segmentation model using leave-one-event-out cross-validation, achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products, the Sentinel-1-based NASA IMPACT model and Google's 10 m Dynamic World water class, which yielded IoUs of 44.1 and 48.1, respectively. UFO is publicly available to support the development and validation of urban inundation mapping methods.