EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification
This dataset addresses the problem of benchmarking transnational few-shot crop classification algorithms for researchers in remote sensing and agriculture, though it is incremental as it builds on existing open-source data.
The authors tackled the lack of a standardized dataset for few-shot crop type classification in Europe by introducing EuroCropsML, which includes 706,683 multi-class labeled data points across 176 classes from Sentinel-2 satellite data for 2021.
We introduce EuroCropsML, an analysis-ready remote sensing machine learning dataset for time series crop type classification of agricultural parcels in Europe. It is the first dataset designed to benchmark transnational few-shot crop type classification algorithms that supports advancements in algorithmic development and research comparability. It comprises 706 683 multi-class labeled data points across 176 classes, featuring annual time series of per-parcel median pixel values from Sentinel-2 L1C data for 2021, along with crop type labels and spatial coordinates. Based on the open-source EuroCrops collection, EuroCropsML is publicly available on Zenodo.