Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade. (arXiv:2007.15129v1 [astro-ph.IM])

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade. (arXiv:2007.15129v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Azari_A/0/1/0/all/0/1">Abigail R. Azari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Biersteker_J/0/1/0/all/0/1">John B. Biersteker</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dewey_R/0/1/0/all/0/1">Ryan M. Dewey</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Doran_G/0/1/0/all/0/1">Gary Doran</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Forsberg_E/0/1/0/all/0/1">Emily J. Forsberg</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Harris_C/0/1/0/all/0/1">Camilla D. K. Harris</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kerner_H/0/1/0/all/0/1">Hannah R. Kerner</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Skinner_K/0/1/0/all/0/1">Katherine A. Skinner</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Smith_A/0/1/0/all/0/1">Andy W. Smith</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Amini_R/0/1/0/all/0/1">Rashied Amini</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cambioni_S/0/1/0/all/0/1">Saverio Cambioni</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Poian_V/0/1/0/all/0/1">Victoria Da Poian</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Garton_T/0/1/0/all/0/1">Tadhg M. Garton</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Himes_M/0/1/0/all/0/1">Michael D. Himes</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Millholland_S/0/1/0/all/0/1">Sarah Millholland</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ruhunusiri_S/0/1/0/all/0/1">Suranga Ruhunusiri</a>

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.

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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