Datasets:
license: cc-by-4.0
size_categories:
- 10M<n<100M
task_categories:
- image-classification
- image-feature-extraction
pretty_name: Copernicus-Pretrain
tags:
- earth-observation
- remote-sensing
- foundation-model
- pretrain
- self-supervised-learning
- sentinel
library_name: datasets
Dataset Card for Copernicus-Pretrain
Copernicus-Pretrain is a large-scale EO pretraining dataset with 18.7M aligned images covering all major Sentinel missions (S1,2,3,5P).
Officially named Copernicus-Pretrain, also referred to as SSL4EO-S ("S" means Sentinel), as an extension of SSL4EO-S12 to the whole Sentinel series.
Dataset Details
Copernicus-Pretrain contains 18.7M aligned imagery from all major Sentinel missions in operation (Sentinel-1 SAR, Sentinel-2 multispectral reflectance, Sentinel-3 multispectral radiance, and Sentinel-5P atmospheric variables), as well as an elevation product Copernicus DEM GLO-30. The images are organized into ~310K regional grids (0.25°x0.25°, consistent with ERA5), densely covering the whole land surface and near-land ocean with eight distinct Sentinel modalities.
| Modality | GSD | Image size | # Grid cells | # Patches | # Timestamps | # Total images | |
|---|---|---|---|---|---|---|---|
| Sentinel-1 GRD | SAR | 10 m | 264×264×2 | 247,723 | 1,067,267 | ~4 | 4,227,387 |
| Sentinel-2 TOA | MS | 10 m | 264×264×13 | 247,723 | 1,067,267 | ~4 | 4,218,065 |
| Sentinel-3 OLCI | MS | 300 m | 96×96×21 | 281,375 | 281,375 | ~8 | 2,189,561 |
| Sentinel-5P CO | atmos. | 1 km | 28×28 | 306,097 | 306,097 | 1–12 | 2,104,735 |
| Sentinel-5P NO2 | atmos. | 1 km | 28×28 | 291,449 | 291,449 | 1–12 | 1,752,558 |
| Sentinel-5P SO2 | atmos. | 1 km | 28×28 | 262,259 | 262,259 | 1–12 | 1,366,452 |
| Sentinel-5P O3 | atmos. | 1 km | 28×28 | 306,218 | 306,218 | 1–12 | 2,556,631 |
| Copernicus DEM | elevation | 30 m | 960×960 | 297,665 | 297,665 | 1 | 297,665 |
| Copernicus-Pretrain | 312,567 | 3,879,597 | 18,713,054 |
Sample Usage
You can load the dataset using the Hugging Face datasets library. This dataset is very large and may require specific handling such as streaming or selecting specific configurations if available.
from datasets import load_dataset
# Load the dataset. For large datasets, consider streaming or specific data_files if available.
# This dataset offers raw GeoTiff and streaming WebDataset formats.
dataset = load_dataset("wangyi111/Copernicus-Pretrain")
# Print the dataset structure (e.g., available splits)
print(dataset)
# Example of accessing a sample from a split (uncomment and adjust if applicable)
# For example, if 'train' split exists:
# print(dataset["train"][0])
License
CC-BY-4.0.
Citation
@misc{wang2025unifiedcopernicusfoundationmodel,
title={Towards a Unified Copernicus Foundation Model for Earth Vision},
author={Yi Wang and Zhitong Xiong and Chenying Liu and Adam J. Stewart and Thomas Dujardin and Nikolaos Ioannis Bountos and Angelos Zavras and Franziska Gerken and Ioannis Papoutsis and Laura Leal-Taixé and Xiao Xiang Zhu},
year={2025},
eprint={2503.11849},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.11849},
}