RapidAI4EO

A Corpus of Dense Time Series Satellite Imagery

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Visibility: Public
Owner: Planet
Created: 26 Jun 2024
Last Updated: 21 Aug 2025

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RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery

The RapidAI4EO corpus is a dataset of dense time series satellite imagery sampled at 500,000 locations across Europe. Sample locations are non-overlapping with a footprint of 600×600 metres. At each location the corpus contains datacubes of two cloud-free, regular-cadence image products and corresponding land cover labels:

Planet Fusion three-metre, five-day cadence radiometrically harmonized and gap-filled imagery for 2018–2019
Sentinel-2 L2A monthly image mosaics at 10-metre resolution for 2018
CORINE Land Cover multiclass labels for 2018

Originally designed to train deep learning models for land use and land cover (LULC) classification and change detection, the corpus is being released as open data to support research in these domains as well as others that could benefit from dense time series satellite imagery.

The corpus was created under the RapidAI4EO project, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.

Download Dataset

This dataset is hosted at the URL https://radiantearth.blob.core.windows.net/mlhub/rapidai4eo/ on Azure Blob Storage. Labels can be found within the labels sub-directory and imagery can be found within the imagery sub-directory. A complete static STAC catalog for the dataset is found within the stac-v1.0 sub-directory. It's recommended to use AzCopy to download the dataset. For example, to download the labels using AzCopy, you can run the following command:

azcopy copy --recursive https://radiantearth.blob.core.windows.net/mlhub/rapidai4eo/labels/ .

Dataset Licenses

Planet Fusion: CC-BY-NC-SA 3.0
Sentinel-2: Copernicus Sentinel Data Terms and Conditions
CORINE Land Cover: Registration and Licensing Conditions of the European Parliament and of the Council on the European Earth monitoring programme

Authors

Timothy Davis
Benjamin Bischke
Patrick Helber
Caglar Senaras
Akhil Rana
Annett Wania
Ruben Van De Kerchove
Daniele Zanaga
Wanda De Keersmaecker
Myroslava Lesiv
Franck Ranera
Giovanni Marchisio

Citation & DOI

Davis, T., Bischke, B., Helber, P., Senaras, C., Rana, A., Wania, A., Van De Kerchove, R., Zanaga, D., De Keersmaecker, W., Lesiv, M., Ranera, F., & Marchisio, G. (2023) "RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/RDNT.GCYDKJ

In BibTeX format:

1@dataset{rapidai4eo,
2  title = {RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery},
3  publisher = {Radiant ML Hub},
4  doi = {https://doi.org/10.34911/RDNT.GCYDKJ},
5  year = {2023},
6  urldate = {<date of access, ISO>},
7  author = {
8    Davis, Timothy and
9    Bischke, Benjamin and
10    Helber, Patrick and
11    Senaras, Caglar and
12    Rana, Akhil and
13    Wania, Annett and
14    Van De Kerchove, Ruben and
15    Zanaga, Daniele and
16    De Keersmaecker, Wanda and
17    Lesiv, Myroslava and
18    Ranera, Franck and
19    Marchisio, Giovanni
20  },
21  version = {1.0},
22}

1@dataset{rapidai4eo,
2  title = {RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery},
3  publisher = {Radiant ML Hub},
4  doi = {https://doi.org/10.34911/RDNT.GCYDKJ},
5  year = {2023},
6  urldate = {<date of access, ISO>},
7  author = {
8    Davis, Timothy and
9    Bischke, Benjamin and
10    Helber, Patrick and
11    Senaras, Caglar and
12    Rana, Akhil and
13    Wania, Annett and
14    Van De Kerchove, Ruben and
15    Zanaga, Daniele and
16    De Keersmaecker, Wanda and
17    Lesiv, Myroslava and
18    Ranera, Franck and
19    Marchisio, Giovanni
20  },
21  version = {1.0},
22}