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, 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.
Dataset ID | ref_cloud_cover_detection_challenge_v1 |
DOI | 10.34911/rdnt.hfq6m7 |
Creator | Radiant Earth Foundation |
Contact | ml@radiant.earth |
Documentation
Tutorial
Citation
Radiant Earth Foundation. (2022). Sentinel-2 Cloud Cover Segmentation Dataset (Version 1). Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.hfq6m7
Python Client example
from radiant_mlhub import Dataset
ds = Dataset.fetch('ref_cloud_cover_detection_challenge_v1')
for c in ds.collections:
print(c.id)
Download Dataset
Source Imagery Collections
Description | Sentinel-2 Cloud Cover Segmentation Train Source Imagery |
License | CC-BY-4.0 |
Collection ID | ref_cloud_cover_detection_challenge_v1_train_source |
Download | |
Description | Sentinel-2 Cloud Cover Segmentation Test Source Imagery |
License | CC-BY-4.0 |
Collection ID | ref_cloud_cover_detection_challenge_v1_test_source |
Download | |
Labels Collections
Description | Sentinel-2 Cloud Cover Segmentation Train Labels |
License | CC-BY-4.0 |
Collection ID | ref_cloud_cover_detection_challenge_v1_train_labels |
Download | |
Description | Sentinel-2 Cloud Cover Segmentation Test Labels |
License | CC-BY-4.0 |
Collection ID | ref_cloud_cover_detection_challenge_v1_test_labels |
Download | |
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