Deep Learning For The Earth Sciences: A Comprehensive Approach To Remote Sensing, Climate Science And Geosciences

"The research in deep learning for the geosciences and Earth observation is growing fast and goes beyond the mere application of algorithms to new data. This is a huge interdisciplinary field. Applying new algorithms to the data deluge is a hot topic in all these cross-sectorial fields. Academic research on this area is strongly involved, and many specialized conferences and special issues in journals are arising each year. The book will provide the reader with the landscape, skills, and principles to quickly become familiar with both fields? needs and applications and will give a principled status of where are we now. The practitioner will be ready to use the technology and principles in his/her own research field in a short period of time. The highlights on future research at the end of each chapter will provide new ideas, particularly for those people involved in advanced research education, who will find these highlights of special interest for PhD Thesis orientations"-- Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices in the field Deep learning is a fundamental technique in modern artificial intelligence and is being applied to disciplines across the scientific spectrum. Earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferate broad spread. Deep Learning for the Earth Sciences delivers a perspective and unique treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described within in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.