Sentinel-2 Cloud Cover Segmentation Dataset

In many uses of multispectral satellite imagery, clouds obscure what we really care about - for example,
tracking wildfires, mapping deforestation, or monitoring crop health. Being able to more accurately remove
clouds from satellite images filters out interference, unlocking the potential of a vast range of use cases.
With this goal in mind, this training dataset was generated as part of [crowdsourcing competition](https://www.drivendata.org/competitions/83/cloud-cover/), and later
on was validated using a team of expert annotators. The dataset consists of Sentinel-2 satellite imagery
and corresponding cloudy labels stored as GeoTiffs. There are 22,728 chips in the training data,
collected between 2018 and 2020.