Section A - Introduction of AI-driven GEE cloud computinge based remote sensing 1. Introduction to Google Earth Engine: A comprehensive workflow 2. Role of GEE in earth observation via remote sensing 3. A meta-analysis of Google Earth Engine in different scientific domains 4. Exploration of science of remote sensing and GIS with GEE 5. Cloud computing platformsebased remote sensing big data applications 6. Role of various machine and deep learning classification algorithms in Google Earth Engine: A comparative analysis 7. Google Earth Engine and artificial intelligence for SDGs Section B - Emerging applications of GEE in Earth observation 8. Machine learning algorithms for air quality and air pollution monitoring using GEE 9. Investigation of surface water dynamics from the Landsat series using Google Earth Engine: A case study of Lake Bafa 10. Monitoring of land cover changes and dust events over the last 2 decades using Google Earth Engine: Hamoun wetland, Iran 11. Leveraging Google Earth Engine for improved groundwater management and sustainability 12. Customized spatial data cube of urban environs using Google Earth Engine (GEE) 13. A novel self-supervised framework for satellite image classification in the Google Earth Engine cloud computing platform 14. Assessment and monitoring of forest fire using vegetation indices and AI/ML techniques over google earth engine 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 16. Time series of Sentinel-1 and Sentinel-2 imagery for parcel-based crop-type classification using Random Forest algorithm and Google Earth Engine 17. Multi-temporal monitoring of impervious surface areas (ISA) changes in an Arctic setting, using ML, remote sensing data, and GEE 18. Estimation of snow or ice cover parameters using Google Earth engine and AI 19. Climate change challenges: The vital role of Google Earth Engine for sustainability of small islands in the archipelagic countries 20. Evaluating machine learning algorithms for classifying urban heterogeneous landscapes using GEE 21. Application of analytic hierarchy process for mapping flood vulnerability in Odisha using Google Earth Engine 22. Deep learning-based method for monitoring precision agriculture using Google Earth Engine 23. Role of AI and IoT in agricultural applications using Google Earth Engine 24. Mature and immature oil palm classification from image Sentinel-2 using Google earth engine (GEE) 25. Tracking land use and land cover changes in Ghaziabad district of India using machine learning and Google Earth engine Section C - Challenges and future trends of GEE 26. Challenges and limitations for cloud-based platforms and integration with AI algorithms for earth observation data analytics 27. AI-driven tools and technologies for agriculture land use & land cover classification using earth observation data analytics

Includes utilization of AI with GEE tools for a spectrum of scientific domains in remote sensing and geographic information systems (GIS) including natural hazard assessment, aquatic and hydrological applications, and forest cover Highlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysis Includes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation