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) techqnieus. It includes a wide range of scientific domains that can utilise remote sensing and geographic information systems (GIS) through detailed case studies. The 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…mehr
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) techqnieus. It includes a wide range of scientific domains that can utilise remote sensing and geographic information systems (GIS) through detailed case studies. The 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 is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilising remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.
Section A: GEE cloud computing based Remote Sensing 1. Cloud computing platforms based remote sensing big data applications 2. Role of GEE in earth observation via remote sensing 3. Applications of GEE in sustainable society and environment 4. Sustainable Remote Sensing Data Analysis using GEE and AI 5. Systematic survey on GEE-based projects and their perspectives Section B: AI-based GEE tool and technologies 6. A comprehensive review of emerging AI-based Machine and Deep learning algorithms for GEE 7. Comparative Analysis of various Machine and Deep learning classification algorithms 8. Estimation of land-use land-cover variations using GEE and AI-based change detection tools 9. Monitoring and mapping of urban development with integration of GEE and AI 10. Image fusion of optical and microwave satellite datasets using deep neural networks 11. AI-driven cloud-based remote sensing for big data analysis Section C: Emerging applications and case studies of GEE in earth observation 12. Remote sensing for Water resource management with GEE 13. Agriculture mapping for crop monitoring using remote sensing and GEE 14. Mapping and monitoring of forest resources and activities using GEE 15. Response to climate change using AI and cloud computing platforms 16. Role of GEE in natural hazard monitoring and management 17. Estimation of Snow/ice cover parameters using GEE and AI Section D: Challenges and future trends of GEE 18. Challenges and limitations of the cloud-based platforms 19. Futuristic AI-driven tools and technologies for earth observation data analytics 20. Exploration of the science of remote sensing and GIS with Google Earth Engine 21. Creative integration of GEE with AI for algorithms to applications
Section A: GEE cloud computing based Remote Sensing 1. Cloud computing platforms based remote sensing big data applications 2. Role of GEE in earth observation via remote sensing 3. Applications of GEE in sustainable society and environment 4. Sustainable Remote Sensing Data Analysis using GEE and AI 5. Systematic survey on GEE-based projects and their perspectives Section B: AI-based GEE tool and technologies 6. A comprehensive review of emerging AI-based Machine and Deep learning algorithms for GEE 7. Comparative Analysis of various Machine and Deep learning classification algorithms 8. Estimation of land-use land-cover variations using GEE and AI-based change detection tools 9. Monitoring and mapping of urban development with integration of GEE and AI 10. Image fusion of optical and microwave satellite datasets using deep neural networks 11. AI-driven cloud-based remote sensing for big data analysis Section C: Emerging applications and case studies of GEE in earth observation 12. Remote sensing for Water resource management with GEE 13. Agriculture mapping for crop monitoring using remote sensing and GEE 14. Mapping and monitoring of forest resources and activities using GEE 15. Response to climate change using AI and cloud computing platforms 16. Role of GEE in natural hazard monitoring and management 17. Estimation of Snow/ice cover parameters using GEE and AI Section D: Challenges and future trends of GEE 18. Challenges and limitations of the cloud-based platforms 19. Futuristic AI-driven tools and technologies for earth observation data analytics 20. Exploration of the science of remote sensing and GIS with Google Earth Engine 21. Creative integration of GEE with AI for algorithms to applications
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