As part of the final BETTER Hackathon, project partners prepared 4 hackathon exercises. WFP organised this exercise as the challenge promoter for the Food Security thematic area. This open exercise featured the use of Binder and purposely provided cloud resources. Participants were expected to be familiar with the Jupyter environment (Python 3) and the most common EO libraries (e.g. GDAL) and were guided to use their favourite approach (e.g. pixel-based or object-based classification) to derive a crop type map for the region using the following combinations of datasets: S2 unfiltered – benchmark, S2 filtered, S2 unfiltered + SAR, S2 filtered + SAR. Libraries used included Rasterio / GDAL, pandas + numpy, scipy, numba, keras / tensorflow / opencv. The recorded part includes the introduction of the exercise in the context of the BETTER project.