EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification
This dataset addresses the need for standardized, transnational data to support data-driven land cover classification in remote sensing and machine learning, though it is incremental as it builds on existing administrative data.
The authors tackled the problem of crop type classification and mapping across Europe by creating EuroCrops, a pan-European dataset based on self-declared field annotations, which includes a new taxonomy scheme (HCAT-ID) to harmonize reference data from administrative sources.
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation. By this, we aim to enrich the research efforts and discussion for data-driven land cover classification via Earth observation and remote sensing. Additionally, through inclusion of self-declarations gathered in the scope of subsidy control from all countries of the European Union (EU), this dataset highlights the difficulties and pitfalls one comes across when operating on a transnational level. We, therefore, also introduce a new taxonomy scheme, HCAT-ID, that aspires to capture all the aspects of reference data originating from administrative and agency databases. To address researchers from both the remote sensing and the computer vision and machine learning communities, we publish the dataset in different formats and processing levels.