Artificial Intelligence on the edge is constrained to low-memory and low-energy environments, but has a high impact potential. We propose a data-centric competition to accelerate the deployment of on-board classification for Earth Observation satellites. To this end, we fix the model architecture a priori to be suitable for ground-to-satellite transmission and on-board inference. The competitors submit model parameters obtained via their training procedure using only a few labeled images taken from the European Space Agency satellite OPS-SAT, and are ranked according to classification accuracy on a larger hidden test set. Our final goals are to alleviate the need for large amounts of in-situ data for training on-board AI, and to reduce the number of pre-processing steps performed on-board. Our approach could be further extended to other domains, including guidance and control, astronomy and more.