Google Earth Engine and Artificial Intelligence for Earth Observation

Vishakha Sood is currently working as a Scientist at the Indian Institute of Technology, Ropar, Punjab, under the Women Scientist Scheme (WOS) of the Department of Science and Technology (DST), Government of India. She is also the founder of Aiotronics Automation Pvt. Ltd., supported under the Himachal Pradesh Chief Minister Startup Scheme. She received her Ph.D. in Electronics and Communication Engineering from Chitkara University, Punjab, in 2020. She earned her B.Tech. degree from Himachal Pradesh University in 2008 and her M.Tech. degree in Electronics and Communication Engineering from Punjab Technical University in 2011. She also completed an MBA in Human Resource Management in 2010. She has extensive academic and research experience and has authored research articles in SCI‑indexed journals and SCOPUS‑indexed book chapters and holds several inventions. Her research interests include satellite sensors, remote sensing, and digital image analysis. She is a Senior Member of IEEE and an active member of professional bodies including the Indian Society of Remote Sensing (ISRS), Indian Society for Technical Education (ISTE), Punjab Science Congress (Punjab Academy of Sciences), IEEE Sensors Council, IEEE Geoscience and Remote Sensing Society, IEEE Women in Engineering, and the European Geosciences Union (EGU). Dileep Kumar Gupta received his doctoral degree from the Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, India. He is currently working as Assistant Professor (Grade II) at Galgotias University, Greater Noida, India. His research expertise lies in microwave active and passive remote sensing, electronics and sensor systems, GNSS-based applications, and algorithm development for soil moisture and crop parameter retrieval using ground‑based and space‑borne platforms. His work closely integrates antenna‑enabled sensing systems with data processing methodologies. He has published extensively in peer‑reviewed journals, conference proceedings, and book chapters, and has also served as an editor for academic books with international publishers. His broader research interests include multi‑sensor remote sensing, microwave system applications, geoinformatics, and the use of artificial intelligence and machine learning techniques in remote sensing data analysis.Dr. Sartajvir Singh is currently serving as Chief Scientific Officer at the Center of Excellence in Socio-Environmental Sustainability for River Sand Mining (SENSRS) and Project Director (ICSSR Project) at Indian Institute of Technology Ropar, India. He is a digital image analyst with expertise in remote sensing and earned his PhD in Electronics & Communication Engineering (Outstanding Thesis Awardee, 2018) after completing his M. Tech (Gold Medalist) and B.Tech with Distinction. He is a Registered Indian Patent & Trademark Agent and a DGCA-approved drone operator, with 70+ innovations (35+ patents granted) and 90+ SCI/Scopus-indexed publications. He has secured over four Crore (INR) research funding, received multiple fellowships, held editorial positions, and is an IEEE Senior Member, advancing electronics, image processing, and geospatial intelligence.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