Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed.
Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.
- Addresses both astronomy and geosciences in parallel, from a big data perspective
- Includes introductory information, key principles, applications and the latest techniques
- Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Researchers and data scientists with focus on Geoscience, Remote sensing, and Astronomy. Computer scientists involved in astronomical and geoscience projects. Graduate students in astronomy and geoscience fields, who are interested in a high level coverage of big data handling in these two fields
- Historical Background of Big Data in Astro and Geo Context
2. Methodology of Knowledge Discovery Processes in Context of Astro and Geo Astroinformatics
3. AstroGeoInformatics: from Data Acquisition to Further Application
4. Surveys, Catalogues, Databases and Archives of Astronomical data
5. Surveys, Catalogues, Databases and Archives of Geoscience data
6. Federation of databases, Virtual observatories
7. High Performance Techniques for Big Data processing
8. Typical Query Processing and Access Methods for Big Astro and Geo Databases
9. Evolutionary Computing , Genetic Algorithms
10. Learning in Big Data: Introduction to ML
11. Adaptive Learning
12. Deep Learning
13. Time Series Analysis
14. Influence on Life Application of Geo-Astro Database Federation
15. Analysis of the Geomagnetic storms by means of Wavelet analysis for identification of the ionospheric effects, Nonparametric spline and multivariate polyspline regression with applications to Big Data in EO
16. Application of databases collected in ionospheric observations by VLF/LF radio signals for detection astro and geo phenomena
17. Transfer of technology to medicine
18. Collection of Examples Glossary References List of related literature
- No. of pages:
- © Elsevier 2020
- 1st January 2020
- Paperback ISBN:
Petr Škoda has been involved in astroinformatics and has a long-term experience in using and lecturing the astronomical Virtual observatory. One of the proposers of COST BigSkyEarth Action and its MC member. BigSkyEarth is the working group behind the idea of the book and a conference about the same topic.
Stellar Department, Astronomical Institute CAS, Czech Republic
Fathalrahman Adam has good understanding of classical machine learning and new concepts, along with hands-on experience and published papers. He is involved in large scale applications using satellite data for earth observation, mainly multi-spectral data. He is a member of BigSkyEarth COST Action.
Deutsches Zentrum für Luft- und Raumfahrt (DLR) German Aerospace Center Earth Observation Center, German Remote Sensing Data Center, Land Surface Oberpfaffenhofen, Germany