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

作　　者：Gabriele Meoni Marcus Martens Dawa Derksen Kenneth See Toby Lightheart Anthony Secher Arnaud Martin David Rijlaarsdam Vincenzo Fanizza Dario Izzo 

机构地区：[1]Department of Space Engineering of the Faculty of Aerospace Engineering,TU Delft,Kluyverweg 1,2629 HS Delft,the Netherlands [2]Advanced Concepts Team,European Space Agency,Keplerlaan 1,2201 AZ Noordwijk,the Netherlands [3]Φ-lab,European Space Agency,Via Galileo Galilei 1,00044,Frascati(RM),Italy [4]Inovor Technologies,SpaceLab Building,Lot Fourteen,Adelaide SA 5000,Australia [5]Capgemini Engineering–Hybrid Intelligence,4-11 Avenue Didier Daurat,Blagnac,France [6]Ubotica Technologies,DCU Alpha,Old Finglas Road 11,Glasnevin,Dublin D11KXN4,Ireland

出　　处：《Astrodynamics》2024年第4期507-528,共22页航天动力学(英文)

摘　　要：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 highquality 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.

关 键 词：OPS-SAT data-centric competition artificial intelligence(AI) onboard machine learning onboard classification 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]