Harnessing Administrative Data Inventories to Create a Reliable
  Transnational Reference Database for Crop Type Monitoring
Maja Schneider Marco Körner
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.