Deep Learning for Earth Observation and Climate Monitoring - Brossura

Brossura

ISBN 10: 0443247129 ISBN 13: 9780443247125

Casa editrice: Elsevier, 2025

Riassunto

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.

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Informazioni sugli autori

Uzair Aslam Bhatti has focused his research on applying machine learning to medical and signal processing problems, with a strong interest in the broader applications of artificial intelligence. He has published nearly 60 academic papers, more than 50 of which are indexed in SCI and EI. During his PhD at Hainan University, he received two Best Research Paper Awards and a Chinese Government Scholarship.

As a Postdoctoral Researcher at the School of Geography (Remote Sensing and Signal Processing), Nanjing Normal University, he published 11 papers in two years as first author (9 SCI journals and 2 conference papers), including articles in leading SCI journals such as IEEE Transactions on Geoscience and Remote Sensing (IF 8.1) and Chemosphere (IF 9.1). His contributions, including 10 SCI papers and two CCF B-level conference papers as first author, earned him recognition as an Excellent Postdoctoral Candidate by Nanjing Normal University.

He has also participated in major projects funded by the National Natural Science Foundation of China, the National Key R&D Program, and the Hainan Provincial Major Science and Technology Program.

Mir Muhammad Nizamani’s research focuses on deep understanding of fundamental ecological principles and methods as well as their applications to current human and urban issues. He has published nearly 60 academic papers and won a Chinese Government Scholarship to pursue a doctorate during his Ph.D. studies at Hainan University. He has participated in many external projects, such as the National Natural Science Foundation of China and the National Science Foundation of Hainan Province.

Yong Wang is a professor at Guizhou University, specializing in ecology and mycology. His research interests encompass a broad range of topics within these fields. As an ecologist, he investigates the relationships between organisms and their environment, studying how living organisms interact with each other and their surrounding ecosystems. With his expertise in ecology and mycology, Professor Yong Wang has contributed to the understanding of the ecological dynamics and functions of fungi, their role in nutrient cycling, symbiotic relationships with other organisms, or the effects of environmental factors on fungal communities. His research findings can help inform conservation efforts, promote sustainable practices, and contribute to the broader scientific knowledge in these fields.

Hao Tang is a Lecturer with the School of Information and Communication Engineering, Hainan University after receiving his Ph.D. degree in mechanical engineering from South China University of Technology, Guangzhou City, Guangdong Province, China, in 2021. His research interests include intelligent manufacturing, industrial big data, scheduling and embedded systems.

Dalla quarta di copertina

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.

Key Features

• 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

About the Editors
Uzair Aslam Bhatti
School of Information and Communication Engineering, Hainan University, P.R. China
Mir Muhammad Nizamani
College of Agriculture, Guizhou University, P.R. China
Yong Wang
College of Agriculture, Guizhou University, P.R. China
Hao Tang
School of Information and Communication Engineering, Hainan University, P.R. China

Dettagli dell'edizione

• Editore: Elsevier
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 0443247129
• ISBN 13: 9780443247125
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 314
• Redattore: Bhatti
• Contatto del produttore: ELSEVIER; https://www.elsevier.com/es-es; ; Mooswaldallee 6; Freiburg; 79108; Germania