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