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

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dc.contributor.authorMeoni, G.
dc.contributor.authorMärtens, M.
dc.contributor.authorDerksen, D.
dc.contributor.authorSee, K.
dc.contributor.authorLightheart, T.
dc.contributor.authorSécher, A.
dc.contributor.authorMartin, A.
dc.contributor.authorRijlaarsdam, D.
dc.contributor.authorFanizza, V.
dc.contributor.authorIzzo, D.
dc.date.issued2024
dc.descriptionPublished: 16 March 2024
dc.description.abstractWhile 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.
dc.description.statementofresponsibilityGabriele Meoni, Marcus Märtens, Dawa Derksen, Kenneth See, Toby Lightheart, Anthony Sécher, Arnaud Martin, David Rijlaarsdam, Vincenzo Fanizza, and Dario Izzo
dc.identifier.citationAstrodynamics, 2024; 8(4):507-528
dc.identifier.doi10.1007/s42064-023-0196-y
dc.identifier.issn2522-008X
dc.identifier.issn2522-0098
dc.identifier.orcidMärtens, M. [0000-0003-1950-7111]
dc.identifier.urihttps://hdl.handle.net/2440/141100
dc.language.isoen
dc.publisherSpringer Nature
dc.rights© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommon s.org/licenses/by/4.0/.
dc.source.urihttp://dx.doi.org/10.1007/s42064-023-0196-y
dc.subjectOPS-SAT; data-centric competition; artificial intelligence (AI); onboard machine learning; onboard classification
dc.titleThe OPS-SAT case: A data-centric competition for onboard satellite image classification
dc.typeJournal article
pubs.publication-statusPublished online

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