README.md

---
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


## Overview

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).

**Total size:** ~175 GB (44 tar archives across 6 tasks)

## 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 | --- |

## Modalities

![Modality examples](previews/preview_modalities.png)

## 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.

![ACA targets](previews/preview_aca.png)

- **Seagrass** (Maldives, 2021): Binary segmentation of seagrass meadows in shallow lagoons. Spatial OOD: Northern/Southern atolls.

![Seagrass targets](previews/preview_seagrass.png)

- **GMW** (global, 1996-2020): Binary segmentation of mangrove extent along tropical/subtropical coasts from Global Mangrove Watch. Spatial OOD: Sundarbans + Everglades + Niger Delta.

![GMW targets](previews/preview_gmw.png)

## 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., ACA)
```bash
huggingface-cli download ayushprd/WaterBench --repo-type dataset --include "data/ACA/*" --local-dir WaterBench
```

### Extract archives
```bash
cd WaterBench/data/ACA
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")
```

## License

This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.