D'Amore / Kerner Machine Learning for Planetary Science

Erscheinungsjahr 2021
ISBN: 978-0-12-818721-0
Verlag: ELSEVIER

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400 Seiten, Kartoniert, Format (B × H): 191 mm x 235 mm

Beschreibung

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.



- 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

Autoren

Herausgeber

Helbert, Joern

D'Amore, Mario

Aye, Michael

Kerner, Hannah

Über den Autor

D'Amore, Mario
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.

Kerner, Hannah
Hannah Kerner is a graduate researcher and PhD candidate at Arizona State University. 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.

Inhaltsverzeichnis

Part I: Introduction to Machine Learning 1. Types of ML methods (supervised, unsupervised, semi-supervised; classification, regression) 2. Dealing with small labeled datasets (semi-supervised learning, active learning) 3. Selecting a methodology and evaluation metrics 4. Interpreting and explaining model behavior 5. Hyperparameter optimization and training neural networks

Part II: Methods of machine learning 6. The new and unique challenges of planetary missions 7. Data acquisition (PDS nodes, etc.) and Data types, projections, processing, units, etc.

Part III: Useful tools for machine learning projects in planetary science 8. The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface 9. Getting data from the PDS, pre-processing, and labeling it

Part IV: Case studies 10. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning and/or Data Restoration 11. Surface mapping via unsupervised learning and clustering of Mercury's Visible-Near-Infrared reflectance spectra 12. Mapping Saturn using deep learning 13. Artificial Intelligence for Planetary Data Analytics - Computer Vision to Boost Detection and Analysis of Jupiter's White Ovals in Images Acquired by the Jiram Spectrometer

Zielgruppe

<p>Graduate students and researchers working in planetary science, especially data analysis and planetary missions</p>

Fachgebiete