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

2020-07-29 · 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

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.

📄 PDF Abstract BibTeX arXiv:2007.15129

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