--- pretty_name: GeoFlood-275 license: cc-by-4.0 task_categories: - image-segmentation language: - en tags: - remote-sensing - flood-mapping - flood-inundation - sentinel-1 - sentinel-2 - sar - optical-sar-fusion - terrain - land-cover - geotiff - webdataset size_categories: - 100K.s2.tif .s1.tif .s1pre.tif .flood.tif .dem.tif .dynamic_landcover.tif .esav200.tif .slope_norm.tif .json ``` Example sample key: ```text EMSR251_02ARENDAL_DEL_MONIT01_v2_0_0 ``` ## Modalities | suffix | source layer | bands | dtype | no-data | notes | | --- | --- | ---: | --- | --- | --- | | `s2.tif` | pre-event Sentinel-2 Level-1C | 4 | Float32 | -100 | B4, B3, B2, B8; stored in clipped reflectance scale, typically 0-6000 | | `s1.tif` | post-event Sentinel-1 GRD | 2 | Float32 | -50 | VV and VH backscatter in dB | | `s1pre.tif` | auxiliary pre-event Sentinel-1 GRD | 2 | Float32 | -50 | optional layer for SAR-SAR or optical+SAR ablation experiments | | `flood.tif` | EMSR-derived inundation reference | 1 | Byte | none | binary event-induced flood mask, 1 = flooded, 0 = non-flood | | `dem.tif` | Copernicus DEM GLO-30 | 1 | Float32 | -9999 | elevation layer aligned to Sentinel-1 reference grid | | `dynamic_landcover.tif` | Dynamic World NRT land-cover composite | 1 | Int16 | 0 | event-adjacent 14-day pre-event majority-vote land-cover labels | | `esav200.tif` | ESA WorldCover v200 2021 | 1 | Int16 | 0 | static 10 m land-cover reference | | `slope_norm.tif` | normalized slope derived from Copernicus DEM | 1 | Float32 | 0 | slope clipped to 0-45 degrees and normalized to 0-1 | | `json` | sample metadata | - | JSON | - | split, source event id, tile coordinates, and original relative paths | The released GeoTIFF files are not pre-normalized training tensors. They preserve the benchmark raster values; normalization is applied in the training dataloader. The reference implementation is `utils/dataloader.py` in the GeoFloodNet/RapidFloodMapping codebase: Sentinel-1 is clipped to [-45, 25] dB and mapped to [-1, 1], Sentinel-2 is clipped to [0, 6000] and mapped to [-1, 1], DEM is scaled by `/5000` then standardized with mean 0.5 and std 0.5, slope is clipped to [0, 1], ESA WorldCover raw codes are remapped to contiguous IDs, Dynamic World labels are kept as integer labels, and flood masks are mapped to {0, 1} when needed. ## Loading Install the common tooling: ```bash python -m pip install -U datasets huggingface_hub rasterio webdataset ``` Stream with Hugging Face Datasets: ```python from datasets import load_dataset repo_id = "jiepanli/GeoFlood-275" data_files = { "train": f"hf://datasets/{repo_id}/data/train/*.tar", "validation": f"hf://datasets/{repo_id}/data/validation/*.tar", "test": f"hf://datasets/{repo_id}/data/test/*.tar", } ds = load_dataset("webdataset", data_files=data_files, streaming=True) sample = next(iter(ds["train"])) print(sample.keys()) ``` Download selected shards and read GeoTIFF bytes with Rasterio: ```python from glob import glob from huggingface_hub import snapshot_download from rasterio.io import MemoryFile import webdataset as wds repo_id = "jiepanli/GeoFlood-275" root = snapshot_download( repo_id=repo_id, repo_type="dataset", allow_patterns=["data/test/*.tar", "metadata/*", "README.md", "NOTICE.md", "CITATION.bib"], ) urls = sorted(glob(f"{root}/data/test/*.tar")) dataset = wds.WebDataset(urls) sample = next(iter(dataset)) with MemoryFile(sample["s2.tif"]) as memfile: with memfile.open() as src: s2 = src.read() # shape: bands x height x width print(sample["__key__"], s2.shape) ``` ## Source Data And Attribution GeoFlood-275 is built from independently retrieved and processed public geospatial products anchored by EMSR event metadata: - Copernicus Emergency Management Service Rapid Mapping EMSR delineation products: https://emergency.copernicus.eu/mapping/ems/rapid-mapping - Sentinel-2 Level-1C imagery from `COPERNICUS/S2`: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 - Sentinel-1 GRD imagery from `COPERNICUS/S1_GRD`: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD - Copernicus DEM GLO-30: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30 - ESA WorldCover v200 2021: https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v200 - Dynamic World V1/NRT land-cover labels: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 Please cite the GeoFlood-275 paper and acknowledge the above source products when using this dataset. Users are responsible for complying with the original source-data terms in addition to the GeoFlood-275 license. ## Limitations The EMSR-derived references are expert-interpreted operational products, not absolute hydrodynamic ground truth. Residual clouds and cloud shadows may remain in some pre-event Sentinel-2 observations because the EMSR-designated pre-event acquisition is retained rather than replaced by an independently selected cloud-free image. ESA WorldCover and Dynamic World are useful but imperfect land-cover references and should not be interpreted as deterministic flood/non-flood constraints. ## Citation ```bibtex @misc{li2026geoflood275, title = {Toward Rapid Flood Mapping Anywhere via Terrain- and Land-Cover-Conditioned Optical-SAR Fusion}, author = {Li, Jiepan and Huang, He and Li, Wenke and Li, Linxin and Xie, Anqi and Ye, Ruoru and Hu, Lei and Hu, Ting and He, Wei and Zhang, Liangpei}, year = {2026}, note = {Manuscript under revision for Remote Sensing of Environment} } ```