“With our new ‘OPS-SAT case’ competition, we seek to take this approach further. Participating teams receive 26 full-sized images acquired by the OPS-SAT CubeSat, which include small 200x200-pixel crops or ‘tiles’ identified with one of eight different classifications – Snow, Cloud, Natural, River, Mountain, Water, Agricultural, or Ice – with a total of ten examples of each type, representing a baseline for feature identification.”

Dario Izzo, heading the ACT, says: “This challenge is an example of AI ‘few-shot learning’. As humans we don’t have to see a lot of cats to learn what is or isn’t a cat, just a few glimpses will be enough. What is needed for future space missions is an AI system that can form a concept from only limited examples given. This is a very challenging and modern problem from the AI point of view, and there is no commonly recognised way of achieving this.”