isp-uv-es/cloud-cover-detection-taco

Sentinel-2 Cloud Cover Detection

This is a repackaging, not a new dataset. It is Sentinel-2 Cloud Cover Detection by Radiant Earth Foundation / DrivenData, 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:

@misc{radiantearth2022cloudcover,
  title     = {Sentinel-2 Cloud Cover Segmentation Dataset},
  author    = {{Radiant Earth Foundation}},
  publisher = {Radiant MLHub},
  version   = {1},
  year      = {2022},
  doi       = {10.34911/RDNT.HFQ6M7}
}

About the data

11748 Sentinel-2 L2A chips of 512x512 at 10 m with four bands (B02, B03, B04, B08) and a binary cloud mask. Data of the DrivenData 'On Cloud N' competition, validated by expert annotators and published by Radiant Earth.

11,748 samples · splits: test 1,262 · train 9,296 · validation 1,190 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Target. A binary cloud mask. Its 255 is the source's own nodata sentinel, preserved as ignore_index rather than folded into 'not cloud'.

Splits. The release's public half only: the competition's private evaluation set was never released, so the release ships every sample as train. split is a constructed 80/10/10 partition that keeps each location on one side, and split_original keeps the release's own value.

Storage. Converted from the original GeoTIFFs, with the four band files stacked into one four-band COG.

Getting started

git clone https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the native reader: needs CMake and Ninja

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/cloud-cover-detection-taco", "cloud-cover-detection.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

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

Metadata without decoding anything:

import taco
taco.read("cloud-cover-detection.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	s2	raster	optical	4 band(s), unit 1, scaled
target	cloud	mask	label_raster	2 classes

Licence

CC-BY-4.0

Providers: Radiant Earth Foundation / DrivenData, 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.