Geospatial Foundation Models: Overview, Application and Benchmarking

Geospatial foundation models (GeoFMs) are a class of large-scale deep learning models, typically based on the transformer architecture, that are pre-trained on vast, diverse datasets of Earth Observation data to learn a general, transferable understanding of the Earth’s surface. These models help address long-standing challenges in Earth Observation by dramatically reducing the need for manually labeled data, handling vast and diverse data streams (e.g., optical, SAR, multispectral, LiDAR), and enabling robust performance across time, space, and sensor types. In this tutorial, we will give an overview of the recent advancements in GeoFMs, highlighting the main challenges in developing these models and differences from foundation models developed for other domains. We will also show practical examples of fine-tuning and inferencing GeoFMs for different downstream tasks using the TerraTorch open-source framework, which facilitates the use of publicly available GeoFMs such as SatMAE, Prithvi-EO, DOFA, Galileo and TerraMind. Finally, we will introduce best practices for systematic and reproducible benchmarking of GeoFMs using the TerraTorch Iterate plug-in and its integration with GEO-Bench.