Corpus ID: 220870795

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

@article{Azari2020IntegratingML,
  title={Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade},
  author={A. Azari and J. Biersteker and R. Dewey and Gary Doran and Emily J. Forsberg and Camilla D. K. Harris and H. Kerner and K. Skinner and Andy W. Smith and R. Amini and S. Cambioni and V. D. Poian and Tadhg M. Garton and M. D. Himes and S. Millholland and S. Ruhunusiri},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
  year={2020}
}
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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