Google Earth Engine and Artificial Intelligence for Earth Observation

Dr. Sood is working as Scientist at Indian Institute of Technology (IIT), Ropar, India, under Women Scientist Scheme (WOS) by Department of Science & Technology (DST), Govt. of India. She is also founder of a company named as Aiotronics Automation Pvt.Ltd. supported under Himachal Pradesh CM Startup Scheme. She has more than 10 years of experience in the field of academics and research. She received her PhD in Electronics and Communication Engineering from Chitkara University, Punjab in 2020. She has done B. Tech from Himachal Pradesh University (HPU) Shimla, 2008 and M. Tech from Punjab Technical University (PTU) in Electronics and Communication Engineering,2011. She has also done MBA in Human Resource (HR) ,2010. She has authored more than 25 SCI-indexed articles (IEEE, T&F, ELSEVIER, and SPRINGER), SCOPUS indexed book chapters and holds many inventions. Her research interests include satellite sensors, remote sensing, scatterometer and digital image analysis.Dileep Kumar Gupta received his doctoral degree from the Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Dr. Dileep received several reputed awards like UGC-NET, GATE, UGC research fellowship and DST international travel support. He has published 30+ research articles in different peer reviewed journals/conference proceedings/book chapters. He is an expert in algorithm development for soil moisture and crop variables retrieval using different ground based and space borne active and passive microwave sensor. He is also an expert of different machine learning algorithms for remote sensing data processing. He is a digital image analyst with a passion for remote sensing. Presently, he is working as a Professor and Associate Director (University Institute of Engineering) at Chandigarh University, Punjab, India. He is also practice as an Indian Patent Agent (IN/PA 5806). He received his PhD (Electronics and Communication Engineering - ECE) from I.K. Gujral Punjab Technical University, Punjab, India in 2018. He received his M.Tech (ECE) as a Gold Medalist, and B.Tech (ECE) with Distinction, from Punjab Technical University in 2011 and 2009, respectively. His research interests include electronics, remote sensing, and digital image processing.Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability.

• Section A - Introduction of AI-driven GEE cloud computingebased remote sensing1. Introduction to Google Earth Engine: A comprehensive workflow2. Role of GEE in earth observation via remote sensing3. A meta-analysis of Google Earth Engine in different scientific domains4. Exploration of science of remote sensing and GIS with GEE5. Cloud computing platformsebased remote sensing big data applications6. Role of various machine and deep learning classification algorithms in Google Earth Engine: A comparative analysis7. Google Earth Engine and artificial intelligence for SDGsSection B - Emerging applications of GEE in Earth observation8. Machine learning algorithms for air quality and air pollution monitoring using GEE9. Investigation of surface water dynamics from the Landsat series using Google Earth Engine: A case study of Lake Bafa10. Monitoring of land cover changes and dust events over the last 2 decades using Google Earth Engine: Hamoun wetland, Iran11. Leveraging Google Earth Engine for improved groundwater management and sustainability12. 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 platform14. Assessment and monitoring of forest fire using vegetation indices and AI/ML techniques over google earth engine15. Utilizing google earth engine and remote sensing with machine learning algorithms for assessing carbon stock loss and atmospheric impact through pre- and postfire analysis16. Time series of Sentinel-1 and Sentinel-2 imagery for parcel-based crop-type classification using Random Forest algorithm and Google Earth Engine17. Multi-temporal monitoring of impervious surface areas (ISA) changes in an Arctic setting, using ML, remote sensing data, and GEE18. Estimation of snow or ice cover parameters using Google Earth engine and AI19. Climate change challenges: The vital role of Google Earth Engine for sustainability of small islands in the archipelagic countries20. Evaluating machine learning algorithms for classifying urban heterogeneous landscapes using GEE21. Application of analytic hierarchy process for mapping flood vulnerability in Odisha using Google Earth Engine22. Deep learning-based method for monitoring precision agriculture using Google Earth Engine23. Role of AI and IoT in agricultural applications using Google Earth Engine24. 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 engineSection C - Challenges and future trends of GEE26. Challenges and limitations for cloud-based platforms and integration with AI algorithms for earth observation data analytics27. AI-driven tools and technologies for agriculture land use & land cover classification using earth observation data analytics