The "OPS-SAT case" dataset is the official dataset of ESA's Kelvins "the OPS-SAT case" challenge, created in collaboration with ESA's Phi-lab and ESA's OPS-SAT spacecraft operations team. It consists of 26 raw and unprocessed images of Earth that have been taken by the OPS-SAT cube-sat using its on-board camera. The images have a resolution of around 2048x1944 or similar and are provided in .png format. The goal of the competition is, given a model of a neural network (in this case the EfficientNet-Lite0), to provide best possible network parameters to perform an on-board classification task. For each of the 8 target classes, there are only 10 labelled images provided, severely limiting the number of "shots" that the neural network has on learning. The size of the labeled patches is 200x200 pixels and the labels are related to the following types of landcover/content: Agricultural, Cloud, Mountain, Natural, River, Sea_Ice, Snow and Water. More details about the competition setup and solution evaluation are on the Kelvins competition platform. The following publication outlines the generation of the dataset: Derksen, D., Meoni, G., Lecuyer, G., Mergy, A., Märtens, M. and Izzo, D. Few-Shot Image Classification Challenge On-Board. NEURIPS2021