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For plastic detection, characterisation and tracking, we need rich spectral information, ideally hyperspectral data. We would like hyperspectral data up to short-wave infrared (2500 nm) and around 0.5 m ground sample distance. Neither of these characteristics are existent in current Earth observation missions, and will still be difficult to find in future Earth observation missions.

The ESA Open Space Innovation Platform Campaign for Cognitive Cloud Computing in Space (3CS) called for project proposals for the use of edge and cloud computing technologies in space. D-Orbit created a consortium including UniBap and Trillium to respond to this opportunity with a proposal to engage the cloud computing community to identify and fly applications on the ION SCV-004 mission.

As humans travel further into the Solar System for longer periods of time, many challenges must be solved. One challenge is ensuring an adequate food supply for the crew. Currently all of the food that is consumed by astronauts on the International Space Station (ISS) is prepackaged on Earth and transported to the ISS. This approach requires a significant investment of mass and the long-term stability of nutrients in the stored food is unclear. Especially the impact of radiation outside of low-Earth orbit may have a negative impact on the stored food.

CubeSats have the potential to revolutionise Earth observation from space by exploring different operating points in terms of spatial, temporal, and spectral resolution. However, physical limitations, mostly in terms of size, prevent them from obtaining high-quality images. OPS-SAT, while characteriaed by a small form factor, provides an impressively capable and flexible hardware/software platform for executing demanding machine learning algorithms (inference stage).