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Deep Learning for Earth Observation and Climate Monitoring

By: (Edited by) Hao Tang , (Edited by) Mir Muhammad Nizamani , (Edited by) Uzair Aslam Bhatti , (Edited by) Yong Wang

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Ksh 24,850.00

Format: Paperback / Softback

ISBN-10: 0443247129

ISBN-13: 9780443247125

Publisher: Elsevier - Health Sciences Division

Imprint: Elsevier - Health Sciences Division

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jun 9th, 2025

Print length: 314 Pages

Weight: 1,000 grams

Product Classification: Earth sciences
Geographical information systems (GIS) & remote sensing
Environmental monitoring
Machine learning

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Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring. This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.

Get Deep Learning for Earth Observation and Climate Monitoring by at the best price and quality guranteed only at Werezi Africa largest book ecommerce store. The book was published by Elsevier - Health Sciences Division and it has pages. Enjoy Shopping Best Offers & Deals on books Online from Werezi - Receive at your doorstep - Fast Delivery - Secure mode of Payment

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