Machine learning in space: extending our reach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-09

AUTHORS

Amy McGovern, Kiri L. Wagstaff

ABSTRACT

We introduce the challenge of using machine learning effectively in space applications and motivate the domain for future researchers. Machine learning can be used to enable greater autonomy to improve the duration, reliability, cost-effectiveness, and science return of space missions. In addition to the challenges provided by the nature of space itself, the requirements of a space mission severely limit the use of many current machine learning approaches, and we encourage researchers to explore new ways to address these challenges. More... »

PAGES

335-340

References to SciGraph publications

  • 2008-10. Automatic detection of dust devils and clouds on Mars in MACHINE VISION AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10994-011-5249-4

    DOI

    http://dx.doi.org/10.1007/s10994-011-5249-4

    DIMENSIONS

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