The need to urgently transition food systems to net-zero, nature-positive that can nourish all people, leaving no one behind is more critical than ever. The COVID-19 pandemic has furthered deepened complex challenges we already face from hunger and nutrition, climate and nature, and societal inequity. Innovation offers a profound opportunity to achieve these transitions and help unlock challenges across food systems.

Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate.