---
title: COP-GEN-Benchmark
canonical_url: "https://www.modelscope.ai/datasets/Major-TOM/COP-GEN-Benchmark"
md_url: "https://www.modelscope.ai/datasets/Major-TOM/COP-GEN-Benchmark.md"
repository: Major-TOM/COP-GEN-Benchmark
last_updated: 2026-08-07
license: cc-by-4.0
storage_size: "329 GB"
downloads: 11217
stars: 0
---

# COP-GEN-Benchmark

> COP-GEN-Benchmark - An open-source dataset by Major-TOM on ModelScope. Evaluation dataset for the COP-GEN paper (arXiv:2603.03239). Enables stochastic-benchmark evaluation of generative EO models by comparing generated sample sets against real multi-temporal…

Major-TOM/COP-GEN-Benchmark is a dataset on ModelScope. totalling 329 GB. licensed under cc-by-4.0.

- **Repository**: Major-TOM/COP-GEN-Benchmark
- **License**: cc-by-4.0
- **Storage size**: 329 GB
- **Downloads**: 11217
- **Stars**: 0
- **Last updated**: 2026-08-07

Source: https://www.modelscope.ai/datasets/Major-TOM/COP-GEN-Benchmark

---

# COP-GEN Benchmark

Evaluation dataset for the COP-GEN paper
([arXiv:2603.03239](https://arxiv.org/abs/2603.03239)).
Enables stochastic-benchmark evaluation of generative EO models by
comparing generated sample sets against real multi-temporal Sentinel-2
observations at 495 geographically diverse locations.

## Subsets

Three parallel subsets with **identical schema** and **shared
georeferencing** (same Major TOM v2 1056x1056 grid):

| Subset      | Source                         | Samples/cell |
|-------------|--------------------------------|--------------|
| `real`      | Sentinel-2 L2A (GEE, cloud-free) | 16 |
| `copgen`    | COP-GEN outputs (subsampled from 33 seeds) | 16 |
| `terramind` | TerraMind outputs              | 16 |

Each sample is a 1056x1056 tile with 12 Sentinel-2 bands
(B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12) at their native resolution (10 / 20 / 60 m), stored as
per-band `uint16` GeoTIFF byte blobs inside each parquet row.

## Quick start

```python
from datasets import load_dataset

# Load one of the three configs
real = load_dataset("Major-TOM/COP-GEN-Benchmark", "real", split="train")
copgen = load_dataset("Major-TOM/COP-GEN-Benchmark", "copgen", split="train")
terramind = load_dataset("Major-TOM/COP-GEN-Benchmark", "terramind", split="train")

# Decode a single band from the first row
import rasterio, io
row = real[0]
with rasterio.open(io.BytesIO(row["B02"])) as src:
    b02 = src.read(1)  # (1056, 1056) uint16
print(row["grid_cell"], row["date"], b02.shape)
```

## Schema

All three subsets share the following columns:

| Column        | Type    | Notes                                       |
|---------------|---------|---------------------------------------------|
| `grid_cell`   | str     | Major TOM cell ID (e.g. `106D_246R`)        |
| `sample_idx`  | int     | 0..15 within cell                           |
| `sample_id`   | str     | STAC product ID (real) or `seed_N` (models) |
| `date`        | str     | ISO date for real; empty for synthetic      |
| `crs`         | str     | UTM CRS (e.g. `EPSG:32734`)                 |
| `ul_x, ul_y`  | float   | v2 grid upper-left in CRS metres            |
| `B01..B12`    | bytes   | per-band GeoTIFF blob, uint16, native res   |
| `thumbnail`   | bytes   | 256x256 JPEG RGB composite                  |

## Reproducing the benchmark evaluation

COP-GEN and TerraMind outputs natively cover the centre 192x192 pixels
(1.92 km) of each 1056 tile. To extract this evaluation footprint
identically across all three subsets, use the provided
`benchmark_footprint.py` utility:

```python
import rasterio, io, json
from metadata.benchmark_footprint import crop_benchmark_footprint, load_grid

grid = load_grid("metadata/benchmark_grid.json")

row = real[0]
with rasterio.open(io.BytesIO(row["B02"])) as src:
    window = crop_benchmark_footprint(src, row["grid_cell"], grid)
    # (1, 192, 192) uint16 — same geographic footprint for all three subsets
```

See `metadata/benchmark_footprint.py` for full documentation of the
cropping convention. The function handles CRS mismatches and is the
exact method used to evaluate the results reported in the paper.

## Metadata

- `metadata/benchmark_grid.json` — cell origins for the 192x192
  evaluation footprint (used by `crop_benchmark_footprint`)
- `metadata/cells.parquet` — per-cell summary (grid_cell, ul_x, ul_y,
  crs, mgrs_tile)
- `metadata/benchmark_footprint.py` — the crop utility

## Citation

```bibtex
@article{copgen2026,
    title={COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data -- Generation Stochastic by Design},
    author={Espinosa, Miguel and Gmelich Meijling, Eva and Marsocci, Valerio and Crowley, Elliot J. and Czerkawski, Mikolaj},
    year={2026},
    journal={arXiv preprint arXiv:2603.03239},
    url={https://arxiv.org/abs/2603.03239},
}
```

## Licensing

- Sentinel-2 data: CC-BY 4.0 (Copernicus).
- COP-GEN outputs: released under CC-BY 4.0 by the authors.
- TerraMind outputs: please check the TerraMind licensing terms before
  redistribution.