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:
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.
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/ .
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
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