Foundation models for the whole planet, as opposed to classical NNs, as opposed to classical geospatial techniques.
I am currently project lead for a CSIRO Geospatial Foundation Model research program, which means that I know quite a lot about this, but have little time to blog it. Yet.
1 Popular Geospatial Foundation Models (as of mid-2025)
The three most relevant, state-of-the-art models for our tasks are Prithvi-V2, Clay, and SatlasPretrain. Each represents a different strategic choice in terms of data, scale, and ecosystem. I’m focussing here on satellite-observation ones, but there are others.
Feature | Prithvi-V2 (IBM/NASA) | Clay | SatlasPretrain (Allen AI) |
---|---|---|---|
Core Architecture & Size | |||
Training Data | |||
Required Bands | |||
Geographic Suitability (Australia) | |||
Applicability to Inundation | |||
Applicability to Burn Scar | |||
Ease of Use (on our 4x H100) | |||
Key Differentiator | Temporal Focus. Designed from the ground up for multi-temporal change detection. | Spectral Focus. Leverages more Sentinel-2 bands for tasks sensitive to specific spectral signatures. | Scale & Generalization. Trained on an unmatched diversity and volume of data; the most powerful general-purpose feature extractor. |
2 Modelling governing equations
See foundation models for PDEs and Schmude et al. (2024). e.g. Prithvi WxC is a weather model surrogate, infilling “earth systems models”.
3 Incoming
Using Foundation Models for Earth Observation — Development Seed (interesting model comparison)
IBM and NASA’s Prithvi project has several model within it (Jakubik et al. 2023; Schmude et al. 2024; Szwarcman et al. 2025)
- NASA-IMPACT/Prithvi-EO-2.0: This repository contains details of the release of the Prithvi-EO-2.0 foundation model.
- IBM and NASA are building an AI foundation model for weather and climate - IBM Research
- NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data | NASA Earthdata
- Prithvi EO 2.0 BurnScars Demo - a Hugging Face Space by ibm-nasa-geospatial
an AWS marketing piece which walks through a workflow helpfully