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Victoria Da Poian

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

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Jul 29, 2020
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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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.

* 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032 
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