Scaling over Space: Advancing the Model and Data Foundations of GeoAI Advances in deep learning and foundation models are raising expectations for general-purpose learning and creating new opportunities to harness the geospatial data revolution for Earth monitoring and scientific discovery, with broad benefits for agriculture, energy, water, transportation, smart cities, and disaster response. At the same time, major challenges remain for large-scale geospatial applications, including spatial variability that substantially weakens model generalization, limited and localized training data, and high computational demands that constrain and slow scientific discovery. This talk will discuss both AI model and data foundations for scaling geospatial applications, including geo-aware learning, knowledge-guided learning, task-aligned pretraining, and new benchmark datasets.
