| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - geospatial |
| - earth-observation |
| - remote-sensing |
| - flood |
| - sentinel-1 |
| - sentinel-2 |
| - sar |
| - dem |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # GEOID-Flood |
|
|
| [](https://arxiv.org/abs/2608.02315) |
| [](https://github.com/links-ads/geoid-flood) |
| [](https://creativecommons.org/licenses/by/4.0/) |
|
|
| 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: |
|
|
| ```bash |
| tar -xf train-s1grd-00000-of-00062.tar -C data/geoid-flood/ |
| ``` |
|
|
| ## Download |
|
|
| `get_data.py` fetches shards and unpacks them into the expected structure, deleting each |
| shard as soon as it is unpacked. It checks free space before starting and resumes if |
| interrupted. |
| ```bash |
| pip install huggingface_hub tqdm |
| 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 |
| python get_data.py --dest data --layer s1grd --workers 8 # more shards in flight |
| ``` |
|
|
| Shards are fetched over the Xet protocol, several at a time. Peak disk usage is the size of |
| your selection plus roughly `--workers` x 2 GB for the shards in flight. |
|
|
| 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](https://creativecommons.org/licenses/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](https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf). |
