Google Earth Engine and Artificial Intelligence for Earth Observation

Dr. Vishakha Sood is working as Scientist at Indian Institute of Technology (IIT), Ropar, Punjab. She has been awarded with Women Scientist (WoS-A) Fellowship from Department of Science & Technology (DST), Govt. of India for three years i.e., 2022-2025. She received her PhD in 2020 for “Detection of Snow/Ice over North Indian Himalayas” using satellite imagery. She has received the M. Tech and B. Tech in Electronics and Communication Engineering from Himachal Pradesh University (in 2008) and M. Tech from Punjab Technical University (in 2011). She has also done MBA (Human Resource) from Institute of Advanced Studies in Education (IASE) Deemed to be University in 2010. She received the "Outstanding Performance Award" in CESA-FIESTA 2023 Symposium from IIT, Ropar. She has more than 10 years of experience in the field of academics, research and administration. She has authored many 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, and digital image analysis. She is IEEE Senior Member and active member of various societies as Indian Society of Remote Sensing (ISRS), Indian Society of Technical Education (ISTE), Punjab Science Congress (Punjab Academy of Sciences) and IEEE Sensors council, Geoscience and Remote Sensing Society Digital Library and IEEE Women in Engineering. She is editing various Special Issues in the Journal Environmental Monitoring and Assessment (EMAS) –Springer and Frontiers in Earth Sciences and in addition, various books with Elsevier.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. 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