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

Camps-valls, Gustau; Tuia, Devis; Zhu, Xiao Xiang; Reichstein, Markus

ISBN 10: 1119646146 / ISBN 13: 9781119646143

Edité par Wiley, 2021

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N° de réf. du vendeur 42789009-n

Détails bibliographiques

• Titre: Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
• Éditeur: Wiley
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 1119646146
• ISBN à 13 chiffres: 9781119646143
• Reliure: Couverture rigide
• État de l'article: New

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Synopsis

DEEP LEARNING FOR THE EARTH SCIENCES

Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices

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 proliferated. Deep Learning for the Earth Sciences delivers 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. The book prepares readers to be ready to use the technologies and principles described 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.

À propos de l?auteur

Gustau Camps-Valls is Professor of Electrical Engineering and Lead Researcher in the Image Processing Laboratory (IPL) at the Universitat de València. His interests include development of statistical learning, mainly kernel machines and neural networks, for Earth sciences, from remote sensing to geoscience data analysis. Models efficiency and accuracy but also interpretability, consistency and causal discovery are driving his agenda on AI for Earth and climate.

Devis Tuia, PhD, is Associate Professor at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He leads the Environmental Computational Science and Earth Observation laboratory, which focuses on the processing of Earth observation data with computational methods to advance Environmental science.

Xiao Xiang Zhu is Professor of Data Science in Earth Observation and Director of the Munich AI Future Lab AI4EO at the Technical University of Munich and heads the Department EO Data Science at the German Aerospace Center. Her lab develops innovative machine learning methods and big data analytics solutions to extract large scale geo-information from big Earth observation data, aiming at tackling societal grand challenges, e.g. Urbanization, UN's SDGs and Climate Change.

Markus Reichstein is Director of the Biogeochemical Integration Department at the Max-Planck- Institute for Biogeochemistry and Professor for Global Geoecology at the University of Jena. His main research interests include the response and feedback of ecosystems (vegetation and soils) to climatic variability with an Earth system perspective, considering coupled carbon, water and nutrient cycles. He has been tackling these topics with applied statistical learning for more than 15 years.