Geospatial Foundation Models

Geospatial foundation models learn reusable representations of places and Earth processes from satellite imagery, maps, environmental measurements, and other spatial data. Common approaches include self-supervised masked prediction and contrastive learning, multimodal and temporal sensor fusion, and adapting pretrained encoders with lightweight fine-tuning or task-specific decoders. These models support tasks from land-cover mapping and disaster monitoring to urban analysis, forecasting, and geospatial reasoning. A central challenge is reliable transfer across regions, sensors, seasons, and tasks: performance can shift substantially, and shared benchmarks, physical validation, and uncertainty assessment remain important.