isp-uv-es/copernicus-bench-cloud-s2-taco

Copernicus-Bench Cloud-S2

This is a repackaging, not a new dataset. It is Copernicus-Bench Cloud-S2 by Technical University of Munich (Copernicus-FM / Copernicus-Bench), CloudSEN12+ (Aybar et al.), ESA Copernicus / Sentinel-2, converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@inproceedings{wang2025copernicusfm,
  title     = {Towards a Unified {Copernicus} Foundation Model for Earth Vision},
  author    = {Wang, Yi and Xiong, Zhitong and Liu, Chenying and Stewart, Adam J. and Dujardin, Thomas and Bountos, Nikolaos Ioannis and Zavras, Angelos and Gerken, Franziska and Papoutsis, Ioannis and Leal-Taix{\'e}, Laura and Zhu, Xiao Xiang},
  booktitle = {2025 IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages     = {9888--9899},
  year      = {2025},
  doi       = {10.1109/ICCV51701.2025.00922}
}
@article{aybar2024cloudsen12plus,
  title   = {{CloudSEN12+}: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in {Sentinel-2}},
  author  = {Aybar, Cesar and Bautista, Lesly and Montero, David and Contreras, Julio and Ayala, Daryl and Prudencio, Fernando and Loja, Jhomira and Ysuhuaylas, Luis and Herrera, Fernando and Gonzales, Karen and Valladares, Jeanett and Flores, Lucy A. and Mamani, Evelin and Qui{\~n}onez, Maria and Fajardo, Rai and Espinoza, Wendy and Limas, Antonio and Yali, Roy and Alc{\'a}ntara, Alejandro and Leyva, Martin and Loayza-Muro, Ra{\'u}l and Willems, Bram and Mateo-Garc{\'i}a, Gonzalo and G{\'o}mez-Chova, Luis},
  journal = {Data in Brief},
  volume  = {56},
  pages   = {110852},
  year    = {2024},
  doi     = {10.1016/j.dib.2024.110852}
}

About the data

Multi-class cloud and cloud-shadow segmentation on Sentinel-2 L1C top-of-atmosphere imagery: 13 bands at 10 m, 512x512. The expert-labelled 25% of CloudSEN12+, as task L1 of Copernicus-Bench.

2,817 samples · splits: test 551 · train 1,699 · validation 567 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Target. A dense expert label over four classes: clear, thick cloud, thin cloud, cloud shadow. The native legend is kept verbatim and additionally reduced to ml:binary contaminated/clear.

Splits. The official splits, preserved.

Getting started

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

import os
from huggingface_hub import hf_hub_download, snapshot_download
from taco.ml import Dataset, plot_sample

path = hf_hub_download("isp-uv-es/copernicus-bench-cloud-s2-taco", "copernicus-bench-cloud-s2.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

ds = Dataset("copernicus-bench-cloud-s2.zip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["s2"].array.shape

Metadata without decoding anything:

import taco
taco.read("copernicus-bench-cloud-s2.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	s2	raster	multispectral	13 band(s), unit 1, scaled
target	cloud	mask	label_raster	4 classes

Licence

CC-BY-4.0

Providers: Technical University of Munich (Copernicus-FM / Copernicus-Bench), CloudSEN12+ (Aybar et al.), ESA Copernicus / Sentinel-2

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.