Machine Learning for Planetary Science

Machine Learning for Planetary Science

1st Edition - March 22, 2022

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  • Editors: Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner
  • Paperback ISBN: 9780128187210
  • eBook ISBN: 9780128187227

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Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation.

Key Features

  • Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials
  • Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets
  • Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems
  • Utilizes case studies to illustrate how machine learning methods can be employed in practice


Graduate students and researchers working in planetary science, especially data analysis and planetary missions

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Foreword
  • References
  • Chapter 1: Introduction to machine learning
  • Abstract
  • 1.1. Overview of machine learning methods
  • 1.2. Supervised learning
  • 1.3. Unsupervised learning
  • 1.4. Semisupervised learning
  • 1.5. Active learning
  • 1.6. Popular machine learning methods
  • 1.7. Data set preparation
  • References
  • Chapter 2: The new and unique challenges of planetary missions
  • Abstract
  • 2.1. Introduction
  • References
  • Chapter 3: Finding and reading planetary data
  • Abstract
  • 3.1. Data acquisition in planetary science
  • Chapter 4: Introduction to the Python Hyperspectral Analysis Tool (PyHAT)
  • Abstract
  • Acronyms
  • Acknowledgements
  • 4.1. Introduction
  • 4.2. PyHAT library architecture
  • 4.3. PyHAT orbital
  • 4.4. PyHAT in-situ
  • 4.5. Conclusion
  • References
  • Chapter 5: Tutorial: how to access, process, and label PDS image data for machine learning
  • Abstract
  • Acknowledgements
  • 5.1. Introduction
  • 5.2. Access to PDS data products
  • 5.3. Preprocessing PDS data products into standard image formats
  • 5.4. Labeling image data
  • 5.5. Example PDS image classifier results
  • 5.6. Summary
  • References
  • Chapter 6: Planetary image inpainting by learning mode-specific regression models
  • Abstract
  • 6.1. Introduction
  • 6.2. Related works
  • 6.3. Experimental data
  • 6.4. Proposed method
  • 6.5. Network architecture
  • 6.6. Experimental results
  • 6.7. Conclusion
  • References
  • Chapter 7: Automated surface mapping via unsupervised learning and classification of Mercury Visible–Near-Infrared reflectance spectra
  • Abstract
  • Acknowledgement
  • 7.1. Introduction
  • 7.2. Mercury and the MASCS instrument
  • 7.3. Data preparation
  • 7.4. Learning from multivariate data
  • References
  • Chapter 8: Mapping storms on Saturn
  • Abstract
  • 8.1. Introduction
  • 8.2. Exploratory principal component analysis
  • 8.3. A deep learning approach
  • 8.4. Saturn's feature map
  • References
  • Chapter 9: Machine learning for planetary rovers
  • Abstract
  • 9.1. Introduction
  • 9.2. Risk- and resource-aware AutoNav
  • 9.3. Drive-by-science
  • 9.4. Demonstration on a test rover
  • 9.5. Conclusion and future work
  • References
  • Chapter 10: Combining machine-learned regression models with Bayesian inference to interpret remote sensing data
  • Abstract
  • 10.1. The need for accurate fast-forward functions
  • 10.2. Bayesian approach to inverse problems
  • 10.3. Machine-learning-based surrogate models
  • 10.4. Case study: constraining the thermal properties of asteroids with surrogate models
  • 10.5. Future perspectives for data fusion
  • References
  • Index

Product details

  • No. of pages: 232
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: March 22, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780128187210
  • eBook ISBN: 9780128187227

About the Editors

Joern Helbert

Joern Helbert has been a staff scientist at the German Aerospace Center since 2003 and is head of the “Planetary spectroscopy group”. He is an expert in planetary remote sensing using infrared techniques. He is involved in several space missions including BepiColombo, MarsExpress, VenusExpress, the NASA MESSENGER mission to Mercury and the JAXA Hayabusa 2 sample return mission. He is Co-Private Investigator of the MERTIS instrument on BepiColombo.

Affiliations and Expertise

Staff Scientist, Institute of Planetary Research, German Aerospace Center, Cologne, Germany

Mario D'Amore

Mario D’Amore has been a staff researcher at the Institute of Planetary Research of the German Aerospace Center (PF-DLR) since 2008.. He is an expert in data analysis, GIS spatial analysis and databases for scientific purposes. Currently, he is the Data Archive and Handling Manager for the MERTIS instrument on the BepiColombo mission at the PF-DLR. He was involved in ESA's Mars and Venus Express Mission as CoI, Data Archive Manager and Calibration Manager for the PFS experiment. Before that, he obtained a fellowship as Guest Scientist at PF-DLR focused on the development of remote sensing data interpretation algorithms, using the data acquired in the Planetary Emissivity Laboratory (PEL) at the PF-DLR.

Affiliations and Expertise

Staff Researcher, Institute of Planetary Research, German Aerospace Center, Cologne, Germany

Michael Aye

Michael Aye is a Research Associate at the Laboratory for Atmospheric and Space Physics, University of Colorado at Boulder. He has been or is currently involved with many missions, including NASA Dawn, Cassini, LRO, MRO, Maven and BepiColombo missions for instrument development, project management, calibration and data analysis. He is Co-Investigator on a NASA Research project and lead analyst on Citizen Science project “Planet Four”. He specializes in cameras, far IR calibration, and image and large data analyses. He is interested in pushing the consolidation of planetary python tools.

Affiliations and Expertise

Research Associate, Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado, USA

Hannah Kerner

Hannah Kerner is an assistant research professor at the University of Maryland in College Park, Maryland in the USA. Her research focuses on machine learning applications for planetary science, specifically novelty detection and change detection. She is a science team member for Mars Science Laboratory (MSL) Curiosity and is on the tactical operations team for the Mars Exploration Rover (MER) Opportunity. She has worked at Planet, a remote sensing company based in San Francisco, as well as NASA’s Jet Propulsion Laboratory, Goddard Space Flight Center, and Langley Research Center. She earned her B.S. in computer science at the University of North Carolina at Chapel Hill, where she conducted research in robot motion planning.

Affiliations and Expertise

Assistant Research Professor, University of Maryland, College Park, Maryland, USA

Ratings and Reviews

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  • Suzanne S. Mon Nov 28 2022

    intro to ML

    this was a helpful start for me during my studies. seeing example of ML used by planetary scientists helped with my homework

  • Mark D. Tue Sep 27 2022

    Useful introduction

    The boon is a good introduction to the topic with several nice examples. Some more ready to use cases would have been nice. Still great to get started