2021/06/14 by Maja Schneider, Amelie Broszeit, Marco Körner · 1 citation
Computer Science · Engineering · #cs.CV #cs.LG #eess.IV
paper · pdf · doi:10.2760/125905
published as Proc. of the 2021 conference on Big Data from Space (BiDS21), 2021, 5, 125-128 · 4 pages, website: https://www.eurocrops.tum.de/
arxiv created 2021/06/14 · arxiv updated 2021/06/16
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.