titleGEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
authorsGaetano Chiriaco, Luca Barco, Andrea Bragagnolo, Claudio Rossi, Edoardo Arnaudo
date2026-08-03
venuearXiv (preprint, not peer reviewed)
idarXiv:2608.02315
affiliationLINKS - AI, Data & Space
upvotes3 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractGeospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data. Concerning flood mapping, existing datasets rarely combine bi-temporal SAR and co-registered optical imagery at scale, leaving the value of foundation models for this downstream task largely untested. We introduce GEOID-Flood, a large-scale multi-modal flood segmentation benchmark, derived from Copernicus Emergency Management Service activations, spanning 219 events across 65 countries over ten years. The dataset provides more than 14,000 tiles with co-registered pre- and post-event Sentinel-1, in GRD and RTC format, pre-event Sentinel-2 composite, and DEM, including manually validated labels that separate background from permanent water and flooded water. Using this benchmark, we evaluate foundation models against conventional encoders across single-image, multi-temporal, and multi-modal protocols. We report three main findings: foundation models offer a consistent but modest advantage; optical-SAR fusion with finetuning best resolves transient flooding; and models trained on GEOID-Flood transfer to unseen events better than those trained on existing datasets. Dataset and code available at https://github.com/links-ads/geoid-flood.

cite

@misc{chiriaco2026geoid,
  title={GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation},
  author={Gaetano Chiriaco and Luca Barco and Andrea Bragagnolo and Claudio Rossi and Edoardo Arnaudo},
  year={2026},
  eprint={2608.02315},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2608.02315}
}