GEOID-Flood

A large-scale multi-modal benchmark for flood segmentation, built from 219 Copernicus EMS Rapid Mapping flood activations across 65 countries (2016-2026).

165,329 rasters / 584 GB across 319 shards. 14,282 tiles: 8,938 train / 1,241 val / 2,674 test, plus 1,429 held-out tiles from activations EMSR857-871 for out-of-distribution evaluation.

Each 1024x1024 tile at 10 m resolution provides co-registered Sentinel-1 GRD and RTC (pre- and post-event, VV/VH), a pre-event Sentinel-2 L2A composite, a Copernicus GLO-30 DEM, and manually validated three-class labels (0 = background, 1 = permanent water, 2 = flooded water).

Three GEOID-Flood events, all layers of a tile side by side. Flooded water is cyan, permanent water blue, invalid pixels gray.

The 219 activations span 65 countries. Splits are assigned per area of interest and touching AoIs share a split, so no flood event straddles train, validation and test.

Layout

Rasters ship as uncompressed tar shards of Cloud-Optimized GeoTIFFs, grouped so you can download only the modalities and splits you need:

{tree}/shards/{split}/{layer}/{split}-{layer}-NNNNN-of-NNNNN.tar

Tar members carry the canonical path (EMSR151-1/s1grd/EMSR151-1-0_s1grd_post_*.tif), so extracting any shard into a tree root rebuilds the layout the training configs expect:

tar -xf train-s1grd-00000-of-00062.tar -C data/geoid-flood/

Download

get_data.py streams shards from the Hub directly into the expected structure

pip install huggingface_hub
python get_data.py --dest data                                     # everything
python get_data.py --dest data --layer s1grd label                 # S1-GRD benchmark
python get_data.py --dest data --tree geoid-flood-heldout \
                   --layer s1rtc label                             # held-out eval only
python get_data.py --list --layer s1grd s2l2a dem label            # preview, no download

Typical selections:

selection	flags	size
everything	(no flags)	~584 GB
S1-GRD single-image benchmark	--layer s1grd label	~205 GB
...train+val only	--layer s1grd label --split train val	~140 GB
early/mid fusion (S1+S2+DEM)	--layer s1grd s2l2a dem label	~392 GB
held-out S1-RTC evaluation	--tree geoid-flood-heldout --layer s1rtc label	~20 GB

Modalities

Every tile carries the same nine layers, all co-registered on one 1024x1024 event-UTM grid at 10 m. Four are imagery, five are derived masks.

layer	bands	passes	size	what it is
s1grd	2 x float32	pre + post	204.6 GB	Sentinel-1 GRD backscatter as linear sigma0, bands ordered VV, VH. One acquisition before the event and one after. The loader converts to dB (10*log10) on read.
s1rtc	2 x float32	pre + post	192.2 GB	The same two acquisitions, radiometrically terrain-corrected, also linear sigma0. NaN marks pixels outside the valid swath.
s2l2a	12 x uint16	pre	177.6 GB	Cloud-filtered pre-event Sentinel-2 L2A surface-reflectance composite, 12 bands.
dem	1 x float32	static	9.5 GB	Copernicus GLO-30 elevation in metres, resampled from 30 m onto the 10 m tile grid.
label	1 x uint8	static	0.1 GB	The training target. Manually validated three classes: 0 background, 1 permanent water, 2 flooded water. 255 marks pixels outside the mapped area and is the ignore index.
cloudmask	1 x uint8	pre	0.1 GB	Cloud and shadow over the S2 composite, from OmniCloudMask: 0 clear, 1 thick cloud, 2 thin cloud, 3 shadow. Not folded into label, so this is the only per-pixel record of cloud.
floodmask	1 x uint8	static	0.1 GB	Binary CEMS Rapid Mapping flood delineation, one of the two products label was derived from. Retains extent the three-class label dropped.
permwater	1 x uint8	static	0.1 GB	Binary permanent-water extent, the other source product behind label. No released config reads it.
validity	1 x uint8	static	0.1 GB	Binary per-pixel validity: 1 where the tile was imaged and mapped. Largely redundant with label == 255.
			584 GB	165,329 rasters

sample/ holds two complete event-AoIs from activation EMSR712 -- EMSR712-10 (train split) and EMSR712-3 (test split) -- with all nine layers for all 47 tiles, 8,076 chip rows, so the loader can be exercised end to end without a full download. Both splits are present on purpose: EMSR712-10 makes fit runnable and EMSR712-3 makes test runnable. It extracts to the same canonical paths, so the only override needed is --data.init_args.metadata_filename data_tiles_s256_st128_sample.csv.

Metadata

file	role
data_tiles_s256_st128.csv	the only metadata the dataloader reads. Enumerates 256x256 chips at stride 128 (train) / 256 (val, test), with valid_proportion, positive_proportion, cloud_cover and split per chip. This is the inventory the published models were trained on
tile_catalog.parquet	the 1024x1024 tile inventory: geometry, UTM CRS, delineation times, is_valid, invalid_pixel_frac and split. Nothing reads it at training time; query it to select events by geography or date. is_valid AND invalid_pixel_frac <= 0.95 is the paper's tile selection -- 12,853 tiles here (8,938 train / 1,241 val / 2,674 test) and 1,429 in geoid-flood-heldout, the counts the paper reports

Citation

See CITATION.cff in the code repository.

License

The GEOID-Flood compilation, splits and labels: CC BY 4.0. Code: MIT, in the code repository. The dem layer is licensed separately (below).

Contains modified Copernicus data. Carry these notices forward when redistributing:

• Modified Copernicus Sentinel-1 and Sentinel-2 data (2016-2026).
• Copernicus Emergency Management Service Rapid Mapping products, © European Union.
• The dem layer is a resampled Copernicus WorldDEM-30 (instance COP-DEM-GLO-30-F), used under the Copernicus WorldDEM-30 licence.