Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.
1. Section A. Introduction of AI-driven GEE cloud computing—based remote sensing - p. 1 2. Chapter 1. Introduction to Google Earth Engine: A comprehensive workflow - p. 3 3. Chapter 2. Role of GEE in earth observation via remote sensing - p. 19 4. Chapter 3. A meta-analysis of Google Earth Engine in different scientific domains - p. 35 5. Chapter 4. Exploration of science of remote sensing and GIS with GEE - p. 49 6. Chapter 5. Cloud computing platforms–based remote sensing big data applications - p. 77 7. Chapter 6. Role of various machine and deep learning classification algorithms in Google Earth Engine: A comparative analysis - p. 89 8. Chapter 7. Google Earth Engine and artificial intelligence for SDGs - p. 115 9. Section B. Emerging applications of GEE in earth observation - p. 133 10. Chapter 8. Machine learning algorithms for air quality and air pollution monitoring using GEE - p. 135 11. Chapter 9. Investigation of surface water dynamics from the Landsat series using Google Earth Engine: A case study of Lake Bafa - p. 177 12. Chapter 10. Monitoring of land cover changes and dust events over the last 2decades using Google Earth Engine: Hamoun wetland, Iran - p. 195 13. Chapter 11. Leveraging Google Earth Engine for improved groundwater management and sustainability - p. 213 14. Chapter 12. Customized spatial data cube of urban environs using Google Earth Engine (GEE) - p. 231 15. Chapter 13. A novel self-supervised framework for satellite image classification in the Google Earth Engine cloud computing platform - p. 249 16. Chapter 14. Assessment and monitoring of forest fire using vegetation indices and AI/ML techniques over google earth engine - p. 269 17. Chapter 15. Utilizing google earth engine and remote sensing with machine learning algorithms for assessing carbon stock loss and atmospheric impact through pre- and postfire analysis - p. 287 18. Chapter 16. Time series of Sentinel-1 and Sentinel-2 imagery for parcel-based crop-type classification using Random Forest algorithm and Google Earth Engine - p. 303 19. Chapter 17. Multi-temporal monitoring of impervious surface areas (ISA) changes in an Arctic setting, using ML, remote sensing data, and GEE - p. 321 20. Chapter 18. Estimation of snow or ice cover parameters using Google Earth engine and AI - p. 349 21. Chapter 19. Climate change challenges: The vital role of Google Earth Engine for sustainability of small islands in the archipelagic countries - p. 367 22. Chapter 20. Evaluating machine learning algorithms for classifying urban heterogeneous landscapes using GEE - p. 379 23. Chapter 21. Application of analytic hierarchy process for mapping flood vulnerability in Odisha using Google Earth Engine - p. 399 24. Chapter 22. Deep learning-based method for monitoring precision agriculture using Google Earth Engine - p. 421 25. Chapter 23. Role of AI and IoT in agricultural applications using Google Earth Engine - p. 449 26. Chapter 24. Mature and immature oil palm classification from image Sentinel-2 using Google earth engine (GEE) - p. 465 27. Chapter 25. Tracking land use and land cover changes in Ghaziabad district of India using machine learning and Google Earth engine - p. 489 28. Section C. Challenges and future trends of GEE - p. 511 29. Chapter 26. Challenges and limitations for cloud-based platforms and integration with AI algorithms for earth observation data analytics - p. 513 30. Chapter 27. AI-driven tools and technologies for agriculture land use & land cover classification using earth observation data analytics - p. 527 31. Index - p. 543
Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.
1. Section A. Introduction of AI-driven GEE cloud computing—based remote sensing - p. 1 2. Chapter 1. Introduction to Google Earth Engine: A comprehensive workflow - p. 3 3. Chapter 2. Role of GEE in earth observation via remote sensing - p. 19 4. Chapter 3. A meta-analysis of Google Earth Engine in different scientific domains - p. 35 5. Chapter 4. Exploration of science of remote sensing and GIS with GEE - p. 49 6. Chapter 5. Cloud computing platforms–based remote sensing big data applications - p. 77 7. Chapter 6. Role of various machine and deep learning classification algorithms in Google Earth Engine: A comparative analysis - p. 89 8. Chapter 7. Google Earth Engine and artificial intelligence for SDGs - p. 115 9. Section B. Emerging applications of GEE in earth observation - p. 133 10. Chapter 8. Machine learning algorithms for air quality and air pollution monitoring using GEE - p. 135 11. Chapter 9. Investigation of surface water dynamics from the Landsat series using Google Earth Engine: A case study of Lake Bafa - p. 177 12. Chapter 10. Monitoring of land cover changes and dust events over the last 2decades using Google Earth Engine: Hamoun wetland, Iran - p. 195 13. Chapter 11. Leveraging Google Earth Engine for improved groundwater management and sustainability - p. 213 14. Chapter 12. Customized spatial data cube of urban environs using Google Earth Engine (GEE) - p. 231 15. Chapter 13. A novel self-supervised framework for satellite image classification in the Google Earth Engine cloud computing platform - p. 249 16. Chapter 14. Assessment and monitoring of forest fire using vegetation indices and AI/ML techniques over google earth engine - p. 269 17. Chapter 15. Utilizing google earth engine and remote sensing with machine learning algorithms for assessing carbon stock loss and atmospheric impact through pre- and postfire analysis - p. 287 18. Chapter 16. Time series of Sentinel-1 and Sentinel-2 imagery for parcel-based crop-type classification using Random Forest algorithm and Google Earth Engine - p. 303 19. Chapter 17. Multi-temporal monitoring of impervious surface areas (ISA) changes in an Arctic setting, using ML, remote sensing data, and GEE - p. 321 20. Chapter 18. Estimation of snow or ice cover parameters using Google Earth engine and AI - p. 349 21. Chapter 19. Climate change challenges: The vital role of Google Earth Engine for sustainability of small islands in the archipelagic countries - p. 367 22. Chapter 20. Evaluating machine learning algorithms for classifying urban heterogeneous landscapes using GEE - p. 379 23. Chapter 21. Application of analytic hierarchy process for mapping flood vulnerability in Odisha using Google Earth Engine - p. 399 24. Chapter 22. Deep learning-based method for monitoring precision agriculture using Google Earth Engine - p. 421 25. Chapter 23. Role of AI and IoT in agricultural applications using Google Earth Engine - p. 449 26. Chapter 24. Mature and immature oil palm classification from image Sentinel-2 using Google earth engine (GEE) - p. 465 27. Chapter 25. Tracking land use and land cover changes in Ghaziabad district of India using machine learning and Google Earth engine - p. 489 28. Section C. Challenges and future trends of GEE - p. 511 29. Chapter 26. Challenges and limitations for cloud-based platforms and integration with AI algorithms for earth observation data analytics - p. 513 30. Chapter 27. AI-driven tools and technologies for agriculture land use & land cover classification using earth observation data analytics - p. 527 31. Index - p. 543
