Challenges and Opportunities of Large Transnational Datasets: A Case Study on European Administrative Crop Data

Maja Schneider, Christian Marchington, Marco Körner

Keywords: datasets, machine learning, agriculture, administrative data, transnational research

TL;DR: Compiling a large-scale dataset comes with a lot of challenges, but is worth the work due to plenty of opportunities.

Abstract: Expansive, informative datasets are vital in providing foundations and possibilities for scientific research and development across many fields of study. Assembly of grand datasets, however, frequently poses difficulty for the author and stakeholders alike, with a variety of considerations required throughout the collaboration efforts and development lifecycle. In this work, we discuss and analyse the challenges and opportunities we faced throughout the creation of a transnational, European agricultural dataset containing reference labels of cultivated crops. Together, this forms a succinct framework of important elements one should consider when forging a dataset of their own.