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

arXiv: https://arxiv.org/abs/2007.15129 · HTML: https://mlsanity.com/paper/2007.15129/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection](https://mlsanity.com/paper/2308.07604/) (2023)
- [Semi-Supervised Learning for Mars Imagery Classification and Segmentation](https://mlsanity.com/paper/2206.02180/) (2022)
- [Machine Learning Applications to Kronian Magnetospheric Reconnection Classification](https://mlsanity.com/paper/2104.00496/) (2021)
- [Rapid Automated Mapping of Clouds on Titan With Instance Segmentation](https://mlsanity.com/paper/2501.04459/) (2025)
- [Science Autonomy using Machine Learning for Astrobiology](https://mlsanity.com/paper/2504.00709/) (2025)
- [Structure-Preserving Unpaired Image Translation to Photometrically Calibrate JunoCam with Hubble Data](https://mlsanity.com/paper/2511.22668/) (2025)

## Abstract (arXiv)

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.