The OPS-SAT case

The OPS-SAT case: A data-centric competition for onboard satellite image classification

Meoni, G. (TU Delft Space Systems Egineering; European Space Agency (ESA))
Märtens, Marcus (European Space Agency (ESA))
Derksen, Dawa (European Space Agency (ESA))
See, Kenneth (Inovor Technologies)
Lightheart, Toby (Inovor Technologies)
Sécher, Anthony (Capgemini Engineering–Hybrid Intelligence)
Martin, Arnaud (Capgemini Engineering–Hybrid Intelligence)
Rijlaarsdam, David (DCU)
Fanizza, Vincenzo (DCU)
Izzo, Dario (European Space Agency (ESA))

2024

While novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly lagging. A major hindrance in this regard is the need for high-quality annotated data for training such systems, which makes the development process of machine learning solutions costly, time-consuming, and inefficient. This paper presents “the OPS-SAT case”, a novel data-centric competition that seeks to address these challenges. The powerful computational capabilities of the European Space Agency’s OPS-SAT satellite are utilized to showcase the design of machine learning systems for space by using only the small amount of available labeled data, relying on the widely adopted and freely available open-source software. The generation of a suitable dataset, design and evaluation of a public data-centric competition, and results of an onboard experimental campaign by using the competition winners’ machine learning model directly on OPS-SAT are detailed. The results indicate that adoption of open standards and deployment of advanced data augmentation techniques can retrieve meaningful onboard results comparatively quickly, simplifying and expediting an otherwise prolonged development period. (Figure presented.).

artificial intelligence (AI)
data-centric competition
onboard classification
onboard machine learning
OPS-SAT

http://resolver.tudelft.nl/uuid:ce22c247-27e3-46ea-a05d-55c720c35085

https://doi.org/10.1007/s42064-023-0196-y

Astrodynamics

Institutional Repository

journal article

© 2024 G. Meoni, Marcus Märtens, Dawa Derksen, Kenneth See, Toby Lightheart, Anthony Sécher, Arnaud Martin, David Rijlaarsdam, Vincenzo Fanizza, Dario Izzo