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arXiv · 2007.15129

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Abigail R. Azari·John B. Biersteker·Ryan M. Dewey·Gary Doran·Emily J. Forsberg·Camilla D. K. Harris·Hannah R. Kerner·Katherine A. Skinner·Andy W. Smith·Rashied Amini·Saverio Cambioni·Victoria Da Poian·Tadhg M. Garton·Michael D. Himes·Sarah Millholland·Suranga Ruhunusiri
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Abstract

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.

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Abigail R. Azari, John B. Biersteker, Ryan M. Dewey, Gary Doran, Emily J. Forsberg, Camilla D. K. Harris, Hannah R. Kerner, Katherine A. Skinner, Andy W. Smith, Rashied Amini, Saverio Cambioni, Victoria Da Poian, Tadhg M. Garton, Michael D. Himes, Sarah Millholland, Suranga Ruhunusiri. 2020-07-29. Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade. https://doi.org/10.3847/25c2cfeb.aa328727

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