Buch, Englisch, 232 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 390 g
Buch, Englisch, 232 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 390 g
ISBN: 978-0-12-818721-0
Verlag: William Andrew Publishing
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.
Zielgruppe
<p>Graduate students and researchers working in planetary science, especially data analysis and planetary missions</p>
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Naturwissenschaften Astronomie Raumfahrt
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Naturwissenschaften Astronomie Sonnensystem: Sonne und Planeten
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Weltraumforschung
Weitere Infos & Material
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