Deep Learning for Earth Observation and Climate Monitoring

Auteur

Uzair Aslam Bhatti is mostly active in the application and development of machine learning in signal processing problems, especially to the application of artificial intelligence in other fields. During his PhD studies at Hainan University, he won 2 Best Research Paper Awards and a Chinese Government Scholarship to pursue a doctorate. During his Post Doc at the School of Geography (Remote Sensing and Signal Processing) of Nanjing Normal University, he published 11 research papers as the first author in two years as the first author, 2 of which were published in the top SCI journals Transactions in Geoscience and Remote Sensing and Chemosphere. Due to the publication of 2 conference papers at CCF B-level conferences and 10 SCI papers as the first author, he was declared an excellent postdoctoral candidate by Nanjing Normal University. He has participated in many projects such as 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.

Texte du rabat

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.

Contenu

1. Introduction: Advancing Ecological Protection Through Integrated GIS-Enabled Environmental Monitoring: A Holistic Approach to Addressing Environmental Pollution

Section I: Deep Learning For Climate Change

2. Secure Data Storage and Processing Architectures for Climate IoT Systems
3. Artificial Intelligence for Remote Sensing and Climate Monitoring
4. Carbon emission pattern analysis and its relationship with climate change

Section II: Deep Learning For Ecological Patterns

5. Application of GIS and remote sensing technology in ecosystem services and biodiversity conservation
6. Unlocking Environmental Secrets with Deep Learning: Pioneering Progress and Uses in India’s Earth Surveillance and Climate Tracking
7. Application of machine learning to urban ecology

Section III: Deep Learning For GIS

8. An integrated deep learning-based approach for traffic maintenance prediction with GIS data
9. Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation

Section IV: Deep Learning For Lulc

10. Enhancing Geospatial Insights: A Data-Driven Approach to Multi-Source Remote Sensing Fusion
11. Climate change air quality monitoring using Sentimental 2 dataset
12. Latest trends in LULC monitoring using Deep Learning

Section V: Deep Learning For Oceans

13. Oceanic Biometric Recognition Algorithm Based on Generalized Zero-Shot Learning
14. Remote Sensing lmage Fusion Based on Deep Learning and Convolutional Neural Network Technique
15. Oil Spills and the Ripple Effect: Exploring Climate and Environmental Impacts Through a Deep Learning Lens