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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.
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