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Full Description
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.
Contents
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
