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.
1. Chapter 1. Introduction: advancing ecological protection through integrated geographic information system-enabled environmental monitoring: a holistic approach to addressing environmental pollution - p. 1 2. Section I: Deep learning for climate change - p. 15 3. Chapter 2. Secure data storage and processing architectures for climate Internet of Things systems - p. 17 4. Chapter 3. Artificial intelligence for remote sensing and climate monitoring - p. 35 5. Chapter 4. Carbon emission pattern analysis and its relationship with climate change - p. 63 6. Abstract - p. 63 7. 4.1 Introduction to carbon emissions and climate change - p. 63 8. 4.2 Historical perspective of carbon emissions - p. 64 9. 4.3 Sector-specific emission patterns - p. 66 10. 4.4 Geographic disparities in emissions - p. 69 11. 4.5 Technological innovations and their impact - p. 71 12. 4.6 Socioeconomic impacts of carbon emissions - p. 74 13. 4.7 Policy and regulatory frameworks - p. 77 14. 4.8 Future outlook and projections - p. 78 15. 4.9 Conclusion - p. 82 16. References - p. 82 17. Section II: Deep learning for ecological patterns - p. 95 18. Chapter 5. Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation - p. 97 19. Chapter 6. Deep learning for environmental insights in India’s earth observation and climate monitoring - p. 123 20. Chapter 7. The application of geographic information systems and remote sensing technologies in urban ecology - p. 137 21. Abstract - p. 137 22. 7.1 Introduction - p. 137 23. 7.2 Fundamentals of geographic information systems and remote sensing technologies - p. 138 24. 7.3 Applications in urban ecology - p. 140 25. 7.4 Case studies - p. 143 26. 7.5 Challenges and limitations - p. 144 27. 7.6 Future directions - p. 146 28. 7.7 Conclusion - p. 149 29. References - p. 149 30. Section III: Deep learning for GIS - p. 165 31. Chapter 8. An integrated deep learning based approach for traffic maintenance prediction with geographic information system data - p. 167 32. Chapter 9. Enriching the metadata of map images: a deep learning approach with geographic information systems-based data augmentation - p. 181 33. Chapter 10. Enhancing geospatial insights: a data-driven approach to multi-source remote sensing fusion - p. 205 34. Section IV: Deep learning for lulc - p. 219 35. Chapter 11. Climate change air quality monitoring using sentimental 2 dataset - p. 221 36. Chapter 12. Latest trends in land use and land cover monitoring using deep learning - p. 237 37. Chapter 13. Oceanic biometric recognition algorithm based on generalized zero shot learning - p. 249 38. Section V: Deep learning for oceans - p. 263 39. Chapter 14. Remote sensing image fusion based on deep learning and convolutional neural network technique - p. 265 40. Chapter 15. Oil spills and the ripple effect: exploring climate and environmental impacts through a deep learning lens - p. 279 41. Index - p. 291
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.
1. Chapter 1. Introduction: advancing ecological protection through integrated geographic information system-enabled environmental monitoring: a holistic approach to addressing environmental pollution - p. 1 2. Section I: Deep learning for climate change - p. 15 3. Chapter 2. Secure data storage and processing architectures for climate Internet of Things systems - p. 17 4. Chapter 3. Artificial intelligence for remote sensing and climate monitoring - p. 35 5. Chapter 4. Carbon emission pattern analysis and its relationship with climate change - p. 63 6. Abstract - p. 63 7. 4.1 Introduction to carbon emissions and climate change - p. 63 8. 4.2 Historical perspective of carbon emissions - p. 64 9. 4.3 Sector-specific emission patterns - p. 66 10. 4.4 Geographic disparities in emissions - p. 69 11. 4.5 Technological innovations and their impact - p. 71 12. 4.6 Socioeconomic impacts of carbon emissions - p. 74 13. 4.7 Policy and regulatory frameworks - p. 77 14. 4.8 Future outlook and projections - p. 78 15. 4.9 Conclusion - p. 82 16. References - p. 82 17. Section II: Deep learning for ecological patterns - p. 95 18. Chapter 5. Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation - p. 97 19. Chapter 6. Deep learning for environmental insights in India’s earth observation and climate monitoring - p. 123 20. Chapter 7. The application of geographic information systems and remote sensing technologies in urban ecology - p. 137 21. Abstract - p. 137 22. 7.1 Introduction - p. 137 23. 7.2 Fundamentals of geographic information systems and remote sensing technologies - p. 138 24. 7.3 Applications in urban ecology - p. 140 25. 7.4 Case studies - p. 143 26. 7.5 Challenges and limitations - p. 144 27. 7.6 Future directions - p. 146 28. 7.7 Conclusion - p. 149 29. References - p. 149 30. Section III: Deep learning for GIS - p. 165 31. Chapter 8. An integrated deep learning based approach for traffic maintenance prediction with geographic information system data - p. 167 32. Chapter 9. Enriching the metadata of map images: a deep learning approach with geographic information systems-based data augmentation - p. 181 33. Chapter 10. Enhancing geospatial insights: a data-driven approach to multi-source remote sensing fusion - p. 205 34. Section IV: Deep learning for lulc - p. 219 35. Chapter 11. Climate change air quality monitoring using sentimental 2 dataset - p. 221 36. Chapter 12. Latest trends in land use and land cover monitoring using deep learning - p. 237 37. Chapter 13. Oceanic biometric recognition algorithm based on generalized zero shot learning - p. 249 38. Section V: Deep learning for oceans - p. 263 39. Chapter 14. Remote sensing image fusion based on deep learning and convolutional neural network technique - p. 265 40. Chapter 15. Oil spills and the ripple effect: exploring climate and environmental impacts through a deep learning lens - p. 279 41. Index - p. 291
