Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications

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. Includes utilisation of AI with GEE tools for a spectrum od scientific domains in remote sensing and geographic information systems (GIS) including natural hazard assessment, aquatic and hydrological applications and forest coverHighlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysisInlcudes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation INDICE: Section A: GEE cloud computing based Remote Sensing1. Cloud computing platforms based remote sensing big data applications2. Role of GEE in earth observation via remote sensing3. Applications of GEE in sustainable society and environment4. Sustainable Remote Sensing Data Analysis using GEE and AI5. Systematic survey on GEE-based projects and their perspectivesSection B: AI-based GEE tool and technologies6. A comprehensive review of emerging AI-based Machine and Deep learning algorithms for GEE7. Comparative Analysis of various Machine and Deep learning classification algorithms8. Estimation of land-use land-cover variations using GEE and AI-based change detection tools9. Monitoring and mapping of urban development with integration of GEE and AI10. Image fusion of optical and microwave satellite datasets using deep neural networks11. AI-driven cloud-based remote sensing for big data analysisSection C: Emerging applications and case studies of GEE in earth observation12. Remote sensing for Water resource management with GEE13. Agriculture mapping for crop monitoring using remote sensing and GEE14. Mapping and monitoring of forest resources and activities using GEE15. Response to climate change using AI and cloud computing platforms16. Role of GEE in natural hazard monitoring and management17. Estimation of Snow/ice cover parameters using GEE and AISection D: Challenges and future trends of GEE18. Challenges and limitations of the cloud-based platforms19. Futuristic AI-driven tools and technologies for earth observation data analytics20. Exploration of the science of remote sensing and GIS with Google Earth Engine21. Creative integration of GEE with AI for algorithms to applications

• ISBN: 978-0-443-27372-8
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 390
• Fecha Publicación: 01/02/2025
• Nº Volúmenes: 1
• Idioma: Inglés