Nebula Public Library

Cubesats have the potential to revolutionize Earth Observation 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 characterized by a small form factor, provides an impressive in terms of capabilities and flexibility hardware/software platform for executing demanding machine learning algorithms (inference stage).

With the “democratisation of space”, and the proliferation of large non-geostationary orbit (NGO) constellations, the pace at which human-made objects are being deployed in orbit demands urgent action in defining and supporting a global Space Traffic Management (STM) plan. Promptly identifying, tracking, and cataloguing of resident space objects (RSOs), in particular after orbit insertion or break-up and fragmentation events, as the recent Russian anti-satellite weapon (ASAT) test put in evidence, is of critical importance to enable a safe and sustainable use of space.

The main objective of this study is the evaluation of the merits of a dual-camera setup for the acquisition of both high spatial resolution images and high-resolution images of identified events of
interest, on-board, through AI methods. A mission analysis of the approach for a single-satellite, dual camera setup as well as for a two-satellite (leading-trailing), single camera setup was

"Don't try this at home" (named after one of the AI ships from I.Banks Culture series) is a concept demonstrating the benefits of having a Cognitive Cloud Computing infrastructure in Space. Such an infrastructure will provide users a comprehensive and scalable set of commercial services to EO sensors and space-based data providers. It enables several applications by efficiently collecting and fusing information on target objects from observations of different platforms at various wavelengths, resolutions, and times. As a result, it leads to timely, precise information.