Deep Learning for Earth Observation and Climate Monitoring
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Form:Einzelkauf Download
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Sprache:Englisch
164,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
19.03.2025
Herausgeber
Uzair Aslam Bhatti + weitereVerlag
Elsevier Science & Techn.Seitenzahl
350 (Printausgabe)
Dateigröße
23294 KB
Sprache
Englisch
EAN
9780443247132
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.
- Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptability
- Includes both learning representations and unsupervised deep learning, covering deep learning picture fusion, regression, parameter retrieval, forecasting, and interpolation from a practical standpoint
- Provides a number of physics-aware deep learning models, including the code and the parameterization of models on a companion website, as well as links to relevant data repositories, allowing readers to test techniques themselves
- Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptability
- Includes both learning representations and unsupervised deep learning, covering deep learning picture fusion, regression, parameter retrieval, forecasting, and interpolation from a practical standpoint
- Provides a number of physics-aware deep learning models, including the code and the parameterization of models on a companion website, as well as links to relevant data repositories, allowing readers to test techniques themselves
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