metadata

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

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

Related Sources

Repository: https://github.com/zhu-xlab/Copernicus-FM
Paper: https://arxiv.org/abs/2503.11849

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}, 
}