Deep Learning for the Earth Sciences

This book offers a comprehensive guide to applying deep learning techniques in earth sciences, making it essential for researchers and students alike.

In Deep Learning for the Earth Sciences, four leading experts provide an insightful exploration of the intersection between deep learning and earth sciences. This book highlights the growing importance of deep learning as a vital technique in modern Artificial Intelligence, emphasizing its applications across various scientific disciplines, including earth science. Despite its significance, the integration of deep learning into earth science curricula is relatively new, making this book a timely resource for those looking to understand this evolving field.

The authors present a comprehensive overview of the concepts, skills, and practices essential for applying deep learning techniques to earth sciences. Readers will find themselves well-prepared to implement the technologies and principles discussed in their own research endeavors. The book is particularly beneficial for advanced research education and provides valuable insights for PhD students seeking guidance in their thesis work.

Additionally, Deep Learning for the Earth Sciences includes a wealth of resources, new ideas, and recommendations for future research. It covers a range of topics such as deep learning for classification, image segmentation, anomaly detection, and domain adaptation. Practical discussions on regression, forecasting, and physics-aware deep learning models further enhance the book's utility. This text is an invaluable addition for researchers, engineers, and students in geosciences, remote sensing, and machine learning, making it a must-have for academic libraries.