Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 3.
6 papers.
- 2023Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detectioncited by 3
- 2022Semi-Supervised Learning for Mars Imagery Classification and Segmentationcited by 2
- 2021Machine Learning Applications to Kronian Magnetospheric Reconnection Classificationcited by 0
- 2025Rapid Automated Mapping of Clouds on Titan With Instance Segmentationcited by 0
- 2025Science Autonomy using Machine Learning for Astrobiologycited by 0
- 2025Structure-Preserving Unpaired Image Translation to Photometrically Calibrate JunoCam with Hubble Datacited by 0
- 2025Structure-Preserving Unpaired Image Translation to Photometrically Calibrate JunoCam with Hubble Data27 Nov 2025
- 2025Science Autonomy using Machine Learning for Astrobiology1 Apr 2025
- 2025Rapid Automated Mapping of Clouds on Titan With Instance Segmentation8 Jan 2025
- 2023Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection15 Aug 2023
- 2022Semi-Supervised Learning for Mars Imagery Classification and Segmentation5 Jun 2022
- 2021Machine Learning Applications to Kronian Magnetospheric Reconnection Classification1 Apr 2021