Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring
This work solves the problem of scalable and reliable ground-truth data for researchers and practitioners in Earth observation and agriculture, though it is incremental as it builds on existing administrative sources.
The paper addresses the bottleneck of inadequate reference data for crop type classification by creating EURO CROPS, a transnational reference dataset that aggregates and harmonizes administrative data from multiple countries, achieving interoperability across regions.
With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain. While the further development of these methods was previously limited by the availability and volume of sensor data and computing resources, the lack of adequate reference data is now constituting new bottlenecks. Since creating such ground-truth information is an expensive and error-prone task, new ways must be devised to source reliable, high-quality reference data on large scales. As an example, we showcase E URO C ROPS, a reference dataset for crop type classification that aggregates and harmonizes administrative data surveyed in different countries with the goal of transnational interoperability.