1. Distributed and Parallel Computing.
2. GEOSS Clearinghouse Integrating Geospatial Resources to Support the Global Earth Observation System of Systems.
3. Using a Cloud Computing Environment to Process Large 3D Spatial Datasets.
4. Building Open Environments to Meet Big Data Challenges in Earth Sciences.
5. Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era.
6. Algorithmic Design Considerations for Geospatial and/or Temporal Big Data.
7. Machine Learning on Geospatial Big Data.
8. Spatial Big Data: Case Studies on Volume, Velocity, and Variety.
9. Exploiting Big VGI to Improve Routing and Navigation Services.
10. Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts.
11. Geoinformatics and Social Media: New Big Data Challenge.
12. Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern.
13. Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web.
14. OGC Standards and Geospatial Big Data.
15. Advanced Deep Learning Models and Algorithms for Spatial-Temporal Data.
16. Deep Learning for Spatial Data: Heterogeneity and Adaptation.
17. Assessing Multilevel Environmental and Air Quality Changes in Australia Pre- and Post-COVID-19 Lockdown: A Spatial Machine Learning Approach Utilizing Earth Observation Data.
18. Fairness-Aware Deep Learning in Space.
19. Integrating Large Language Models and Qualitative Spatial Reasoning.
20. Toward a Spatial Metaverse: Building Immersive Virtual Experiences with Georeferenced Digital Twin and Game Engine.
21. A Topological Machine Learning Approach with Multichannel Integration for Detecting Geospatial Objects.