Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-classification
+  - image-segmentation
+language:
+  - en
+tags:
+  - remote-sensing
+  - sentinel-2
+  - sentinel-1
+  - sentinel-3
+  - marine
+  - coastal
+  - benchmark
+  - geospatial
+  - foundation-models
+  - ocean
+  - earth-observation
+size_categories:
+  - 100K<n<1M
+pretty_name: WaterBench
+---
+
+# WaterBench: Evaluating Geospatial Foundation Models for Coastal and Marine Tasks
+
+**Paper:** KDD 2026 | **Authors:** Ayush Prasad, Stefan Oehmcke (University of Rostock)
+
+## Overview
+
+Coastal and marine environments play a critical role in climate regulation, biodiversity, and the global economy. Satellite remote sensing provides consistent large-scale observation of these areas, yet utilizing representation learning for marine domains remains under-explored. Existing geospatial foundation models (GFMs) are primarily evaluated on terrestrial tasks and lack comprehensive marine benchmarks.
+
+WaterBench is a benchmark and evaluation protocol for assessing GFMs on high-resolution (10m) radar and optical satellite imagery (Sentinel-1, Sentinel-2) across two downstream task families: image-level regression and classification (e.g., water quality, bathymetry, oil-slick detection) and pixel-level segmentation (e.g., mangroves, seagrass). We also include 300m Sentinel-3 ocean-color imagery as contextual information. WaterBench spans multiple coastal regions and seasons, defining fixed splits for in-distribution (ID) evaluation and held-out tests that are spatial (new geographies) and, where applicable, temporal (new years).
+
+## Tasks
+
+| Task | Resolution | Modalities | Target / Classes | Metrics | Train | Val | Test | Spatial | Temporal |
+|------|-----------|------------|-----------------|---------|-------|-----|------|---------|----------|
+| OceanState | 10 km | S1, S2, S3 | Continuous (SST, SSS, SSH, CHL, O2) | RMSE, R² | 1,400 | 200 | 400 | 200 | 200 |
+| GEBCO | 500 m | S1, S2 | Continuous (depth) | RMSE, R² | 5,000 | 800 | 1,600 | 800 | 800 |
+| Oil Slick | 10 m | S1, S2, S3 | Binary (oil/non-oil) | Acc, F1 | 900 | 150 | 300 | 150 | --- |
+| ACA | 10 m | S1, S2 | Multi-class (benthic: 7, geomorphic: 12) | Acc, mIoU | 8,211 | 2,719 | 2,658 | 879 | --- |
+| Seagrass | 10 m | S1, S2 | Binary (presence/absence) | Acc, mIoU | 832 | 114 | 222 | 124 | --- |
+| GMW | 10 m | S1, S2 | Binary (mangrove extent) | Acc, mIoU | 11,012 | 1,835 | 3,670 | 1,820 | --- |
+
+## Task Descriptions
+
+### Image-Level Tasks
+
+- **OceanState** (global, 2017-2023): Multi-target regression predicting five ocean variables (SST, SSS, SSH, CHL, O2) from Sentinel-1/2/3 imagery. Labels from Copernicus Marine Service (CMEMS). Spatial OOD: Polar biome. Temporal OOD: 2022-2023.
+
+- **GEBCO** (global, 2025 grid): Depth regression from satellite imagery in shallow coastal waters. Labels from GEBCO 2025 global bathymetry grid. Spatial OOD: Mediterranean Sea. Temporal OOD: 2022-2023.
+
+- **Oil Slick** (global, 2017-2023): Binary classification of oil slicks vs. lookalikes. Labels from GlobalOSD-SAR dataset. Spatial OOD: Mediterranean Sea.
+
+### Pixel-Level Tasks
+
+- **ACA** (global, 2018-2020): Coral reef segmentation into geomorphic zones and benthic classes following Allen Coral Atlas taxonomy. Spatial OOD: Great Barrier Reef + Bermuda.
+
+- **Seagrass** (Maldives, 2021): Binary segmentation of seagrass meadows in shallow lagoons. Spatial OOD: Northern/Southern atolls.
+
+- **GMW** (global, 1996-2020): Binary segmentation of mangrove extent along tropical/subtropical coasts from Global Mangrove Watch. Spatial OOD: Sundarbans + Everglades + Niger Delta.
+
+## Data Format
+
+All images are stored as GeoTIFF files with LZW compression, packaged in uncompressed `.tar` archives.
+
+### Sentinel-2 (images_s2)
+- 12 bands: B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12, B01, B09
+- 224x224 pixels at 10m resolution (resampled from native resolutions)
+- Surface reflectance values scaled to [0, 1]
+
+### Sentinel-1 (images_s1)
+- 2 bands: VV, VH (backscatter)
+- 224x224 pixels at 10m resolution
+
+### Sentinel-3 (images_s3) — OceanState, Oil Slick only
+- 16 OLCI bands (Oa01-Oa12, Oa16-Oa18, Oa21)
+- 120x120 pixels at 300m resolution
+
+### Labels
+- **OceanState/GEBCO:** Continuous values in `metadata.csv` columns
+- **Oil Slick:** Binary label in `metadata.csv`
+- **ACA:** Per-pixel GeoTIFF masks (benthic: 7 classes, geomorphic: 12 classes)
+- **Seagrass/GMW:** Per-pixel binary GeoTIFF masks
+
+## Repository Structure
+
+```
+data/
+├── OceanState/
+│   ├── metadata.csv
+│   ├── metadata.json
+│   ├── splits/{random,geographic,temporal}/{train,val,test}.txt
+│   ├── OceanState-images_s2.tar
+│   ├── OceanState-images_s1.tar
+│   └── OceanState-images_s3.tar
+├── GEBCO/
+│   ├── metadata.csv, metadata.json, splits/
+│   ├── GEBCO-images_s2-{00,01}.tar
+│   └── GEBCO-images_s1.tar
+├── OilSlick/
+│   ├── metadata.csv, metadata.json, splits/
+│   ├── OilSlick-images_s2-{00,...}.tar
+│   ├── OilSlick-images_s1-{00,01}.tar
+│   └── OilSlick-images_s3.tar
+├── ACA/
+│   ├── metadata.csv, metadata.json, splits/
+│   ├── ACA-images_s2-{00,...}.tar
+│   ├── ACA-images_s1.tar
+│   ├── ACA-labels_benthic.tar
+│   └── ACA-labels_geomorphic.tar
+├── Seagrass/
+│   ├── metadata.csv, metadata.json, splits/
+│   ├── Seagrass-images_s2.tar
+│   ├── Seagrass-images_s1.tar
+│   └── Seagrass-labels.tar
+└── GMW/
+    ├── metadata.csv, metadata.json, splits/
+    ├── GMW-images_s2-{00,...}.tar
+    ├── GMW-images_s1-{00,01}.tar
+    └── GMW-labels.tar
+```
+
+## Download
+
+### Full dataset
+```bash
+huggingface-cli download ayushprd/WaterBench --repo-type dataset --local-dir WaterBench
+```
+
+### Single task (e.g., OceanState)
+```bash
+huggingface-cli download ayushprd/WaterBench --repo-type dataset --include "data/OceanState/*" --local-dir WaterBench
+```
+
+### Extract archives
+```bash
+cd WaterBench/data/OceanState
+for f in *.tar; do tar xf "$f"; done
+```
+
+## Splits
+
+Each task provides pre-defined train/val/test splits as text files listing sample IDs (one per line).
+
+- **`splits/random/`** — Standard i.i.d. split
+- **`splits/geographic/`** — Spatial OOD: held-out geographic regions in test set
+- **`splits/temporal/`** — Temporal OOD: held-out years in test set (OceanState, GEBCO only)
+
+### Loading splits in Python
+
+```python
+from pathlib import Path
+
+task_dir = Path("WaterBench/data/OceanState")
+train_ids = task_dir.joinpath("splits/random/train.txt").read_text().strip().split("\n")
+val_ids = task_dir.joinpath("splits/random/val.txt").read_text().strip().split("\n")
+test_ids = task_dir.joinpath("splits/random/test.txt").read_text().strip().split("\n")
+```
+
+## Citation
+
+```bibtex
+@inproceedings{prasad2026waterbench,
+  title={WaterBench: Evaluating Geospatial Foundation Models for Coastal and Marine Tasks},
+  author={Prasad, Ayush and Oehmcke, Stefan},
+  booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
+  year={2026},
+  publisher={ACM},
+  doi={10.1145/3690624}
+}
+```
+
+## License
+
+This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.