Machine Learning for Planetary Science
- ISBN-10
- 0128187220
- ISBN-13
- 9780128187227
- Category
- Science
- Pages
- 232
- Language
- English
- Published
- 2022-03-25
- Publisher
- Elsevier
- Authors
- Michael Aye, Joern Helbert, Mario D'Amore
Description
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
Get the book
Similar books
-
Machine Learning Techniques for Space Weather
By Jay Johnson, Enrico Camporeale, Simon WingAs this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks ...
-
Machine Learning in Heliophysics
By Thomas Berger, Enrico Camporeale, Bala PoduvalMachine Learning in Heliophysics
-
Planetary Remote Sensing and Mapping
By Bo Wu, Kaichang Di, Jürgen OberstPlanetary Remote Sensing and Mapping introduces original research and new developments in the areas of planetary remote sensing, photogrammetry, mapping, GIS, and planetary science resulting from the recent space exploration missions.
-
Machine Learning
By Yagang ZhangMachine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book.
-
Machine Learning and Artificial Intelligence in Geosciences
Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a ...
-
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data
By Željko Ivezić, Andrew J. Connolly, Alexander Gray... μ F = F(ρ) and a standard deviation σF = (N − 3)−1/2. For the above sample with N = 10 and r = 0.72, this approximate approach gives a significance level of 0.8% when ρ = 0 (instead of the exact value of 1%). Pearson's correlation ...
-
Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part...
By Panos Pardalos, Giuseppe Nicosia, Renato UmetonIntell. Syst. Technol. (TIST) 2(4), 1–22 (2011) 9. Emami, E., Ahmad, T., Bebis, G., Nefian, A., Fong, T.: On crater classification using deep convolutional neural networks. In: Lunar and Planetary Science Conference, vol. 49 (2018) 10.
-
Large Meteorite Impacts and Planetary Evolution VI
By Christian Koeberl, Wolf Uwe ReimoldJones, E., 2019, A battle between machine learning, traditional clustering and citizen scientists in the detection and segmentation of polar spring-time fans on Mars, in Proceedings of the 19th Australian Space ...
-
The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
By Kate CrawfordHow is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality.
-
Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada, October 4-6, 2015. Proceedings
By Stan Matwin, Nathalie JapkowiczMachine. Learning. by. Domain. Experts. Kiri L. Wagstaff Jet Propulsion Laboratory, California Institute of ... volumes of scientific data being collected by fields such as astronomy, biology, planetary science, medicine, etc., ...