Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

Deep learning is a fundamental technique in modern AI and is being applied to disciplines across the scientific spectrum, including Earth science. This book provides a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. It includes an introduction to deep learning for classification purposes, learning representations and unsupervised deep learning, practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation, and physics-aware deep learning models. Perfect for PhD students and researchers in geosciences, image processing, remote sensing, electrical engineering, and computer science, and machine learning.

Format: Hardback
Length: 432 pages
Publication date: 26 August 2021
Publisher: John Wiley and Sons Ltd

Deep learning is a powerful technique in modern artificial intelligence that is being applied to various disciplines, including earth science. While the relationship between deep learning and earth sciences has gained attention in recent years, it has not yet been widely integrated into academic curricula.

Deep Learning for the Earth Sciences provides a comprehensive and insightful exploration of the concepts, skills, and practices required to effectively apply deep learning techniques to earth science. The book is designed to prepare readers to utilize the technologies and principles described in their own research.

The editors of the book are distinguished experts in the field, and they have included valuable resources that offer explanations, new ideas, and recommendations for future research. These resources are particularly useful for advanced research education and individuals pursuing PhD thesis orientations.

The book covers a wide range of topics, including an introduction to deep learning for classification purposes, advancements in image segmentation, encoding priors, anomaly detection, target detection, and domain adaptation. It also delves into learning representations and unsupervised deep learning, such as deep learning image fusion, image retrieval, matching, and co-registration. Practical discussions on regression, fitting, parameter retrieval, forecasting, and interpolation are included, as well as an examination of physics-aware deep learning models that can emulate complex codes and model parametrizations.

Deep Learning for the Earth Sciences is an essential resource for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering, and computer science. It offers a comprehensive and up-to-date understanding of deep learning techniques and their applications to earth science, enabling readers to stay at the forefront of this rapidly evolving field.

Weight: 906g
Dimension: 250 x 175 x 32 (mm)
ISBN-13: 9781119646143