{"product_id":"9781119646143","title":"Deep Learning for the Earth Sciences A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences","description":"\u003ch1\u003eDeep Learning for the Earth Sciences\u003c\/h1\u003e\u003ch2\u003eA Comprehensive Approach to Remote Sensing, Climate Science and Geosciences\u003c\/h2\u003e\u003ch3\u003eGustau Camps-Valls | Devis Tuia | Xiao Xiang Zhu | Markus Reichstein\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eTechnology \u0026amp; Engineering \/ Remote Sensing \u0026amp; Geographic Information Systems\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003eDEEP LEARNING FOR THE EARTH SCIENCES \u003cp\u003e\u003cb\u003eExplore this insightful treatment of deep learning in the field of earth sciences, from four leading voices\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eDeep 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. \u003ci\u003eDeep Learning for the Earth Sciences\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eThe 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: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation\u003c\/li\u003e\n\u003cli\u003eAn exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration\u003c\/li\u003e\n\u003cli\u003ePractical discussions of regression, fitting, parameter retrieval, forecasting and interpolation\u003c\/li\u003e\n\u003cli\u003eAn examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, \u003ci\u003eDeep Learning for the Earth Sciences\u003c\/i\u003e will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eGustau Camps-Valls\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDevis Tuia, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eXiao Xiang Zhu\u003c\/b\u003e 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. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eMarkus Reichstein\u003c\/b\u003e 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. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e23 August 2021\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119646143\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e432\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e24.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44315523711116,"sku":"9781119646143","price":143.06,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119646143_b46200b2-487e-44e4-bb30-59890f4acdb4.jpg?v=1780114660","url":"https:\/\/lateknightbooks.com\/products\/9781119646143","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}