Establishing sustainable agricultural systems while ensuring food security has become a global priority. Achieving this goal requires contributions from various fields of agricultural science, many of which rely on detailed information about crops. Recent advances in deep learning and the transnational harmonization of administrative data have led to the availability of increasingly large datasets containing polygons of agricultural land. However, these datasets vary in quality and detail. Alexander Naumann, Sven Gedicke, and Jan Hendrik Haunert explain how data fusion can be used to achieve synergies between different sources of information and evaluate the quality of model results in their recently published article “A Scalable Matching Approach for the Comparison of Agricultural Land Use Maps Based on Corresponding Field Polygons” in the current issue of the International Journal of Digital Earth. Enjoy reading!