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The EUROCROPSML time series benchmark dataset for few-shot crop type classification in Europe
Joana Reuss1, Jan Macdonald2, Simon Becker2,3
1Technical University of Munich, Chair of Remote Sensing Technology, Munich, 80333, Germany. joana.reuss@tum.de.
Abstract:
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.
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