The EuroCropsML time series benchmark dataset for few-shot crop type classification in Europe
We introduce EUROCROPSML, an analysis-ready remote sensing dataset based on the open-source EUROCROPS collection, for machine learning (ML) benchmarking of time series crop type classification in Europe. It is the first time-resolved remote sensing dataset designed to benchmark transnational few-shot crop type classification algorithms that supports advancements in algorithmic development and research comparability. It comprises 706683 multi-class labeled data points across 176 crop classes. Each data point features a time series of per-parcel median pixel values extracted from Sentinel-2 L1C data and precise geospatial coordinates. EUROCROPSML is publicly available on Zenodo.
Authors
- Simon Becker (ORCID: https://orcid.org/0000-0002-6703-9511)
- Jan Macdonald (ORCID: https://orcid.org/0000-0002-6657-7481)
- Joana Reuss (ORCID: https://orcid.org/0000-0001-5842-0356)
- Lorenz Richter (ORCID: https://orcid.org/0000-0001-5028-5639)
- Marco Körner (ORCID: https://orcid.org/0000-0002-9186-4175)
Institutions
- Zuse Institute Berlin (DE)
- ETH Zurich (CH)
- Remote Sensing Solutions (Germany) (DE)
- Deutsches Institut für Normung (DE)
- Technical University of Munich (DE)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-04-19
- DOI
- https://doi.org/10.1038/s41597-025-04952-7
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72