Machine Learning for Planetary Science 1st Edition Joern Helbert

Machine Learning for Planetary Science – Ebook Instant Download/Delivery ISBN(s): 9780128187210,0128187212

Product details:

• ISBN-10 ‏ : ‎ 0128187212
• ISBN-13 ‏ : ‎ 978-0128187210
• Author(s): Joern Helbert, Mario D’Amore, Michael Aye, Hannah Kerner

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.

Table contents:

Chapter 1: Introduction to machine learning
Chapter 2: The new and unique challenges of planetary missions
Chapter 3: Finding and reading planetary data
Chapter 4: Introduction to the Python Hyperspectral Analysis Tool (PyHAT)
Chapter 5: Tutorial: how to access, process, and label PDS image data for machine learning
Chapter 6: Planetary image inpainting by learning mode-specific regression models
Chapter 7: Automated surface mapping via unsupervised learning and classification of Mercury Visible–Near-Infrared reflectance spectra
Chapter 8: Mapping storms on Saturn
Chapter 9: Machine learning for planetary rovers
Chapter 10: Combining machine-learned regression models with Bayesian inference to interpret remote sensing data
References
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