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
4 References
Allen, Markou, Tebbutt, et al. 2025. “End-to-End Data-Driven Weather Prediction.”
Bodnar, Bruinsma, Lucic, et al. 2024. “Aurora: A Foundation Model of the Atmosphere.”
Chen, Kun, Bai, Ling, et al. 2024. “Towards an End-to-End Artificial Intelligence Driven Global Weather Forecasting System.”
Cheng, Min, Liu, et al. 2025. “TorchDA: A Python Package for Performing Data Assimilation with Deep Learning Forward and Transformation Functions.” Computer Physics Communications.
Chen, Kang, Han, Gong, et al. 2023. “FengWu: Pushing the Skillful Global Medium-Range Weather Forecast Beyond 10 Days Lead.”
Chen, Lei, Zhong, Zhang, et al. 2023. “FuXi: A Cascade Machine Learning Forecasting System for 15-Day Global Weather Forecast.” Npj Climate and Atmospheric Science.
Chu, Tian, Wang, et al. 2021. “Twins: Revisiting the Design of Spatial Attention in Vision Transformers.” In Advances in Neural Information Processing Systems.
Duraisamy, Iaccarino, and Xiao. 2019. “Turbulence Modeling in the Age of Data.” Annual Review of Fluid Mechanics.
Grohs, and Herrmann. 2022. “Deep Neural Network Approximation for High-Dimensional Elliptic PDEs with Boundary Conditions.” IMA Journal of Numerical Analysis.
Guibas, Mardani, Li, et al. 2021. “Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.”
Guo, Jia, and Bai. 2022. “Transformer Based on Channel-Spatial Attention for Accurate Classification of Scenes in Remote Sensing Image.” Scientific Reports.
Hoffimann, Zortea, de Carvalho, et al. 2021. “Geostatistical Learning: Challenges and Opportunities.” Frontiers in Applied Mathematics and Statistics.
Huang, Gianinazzi, Yu, et al. 2024. “DiffDA: A Diffusion Model for Weather-Scale Data Assimilation.”
Jakubik, Roy, Phillips, et al. 2023. “Foundation Models for Generalist Geospatial Artificial Intelligence.”
Keisler. 2022. “Forecasting Global Weather with Graph Neural Networks.”
Keller, and Potthast. 2024. “AI-Based Data Assimilation: Learning the Functional of Analysis Estimation.”
Lam, Sanchez-Gonzalez, Willson, et al. 2023a. “GraphCast: Learning Skillful Medium-Range Global Weather Forecasting.”
———, et al. 2023b. “Learning Skillful Medium-Range Global Weather Forecasting.” Science.
Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators.”
Manshausen, Cohen, Pathak, et al. 2024. “Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales.”
McNally, Lessig, Lean, et al. 2024. “Data Driven Weather Forecasts Trained and Initialised Directly from Observations.”
Mickisch, Klemmer, Teng, et al. 2025. “A Joint Space-Time Encoder for Geographic Time-Series Data.” In.
Pathak, Subramanian, Harrington, et al. 2022. “Fourcastnet: A Global Data-Driven High-Resolution Weather Model Using Adaptive Fourier Neural Operators.”
Price, Sanchez-Gonzalez, Alet, et al. 2024. “GenCast: Diffusion-Based Ensemble Forecasting for Medium-Range Weather.”
Rasp, Hoyer, Merose, et al. 2024. “WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models.”
Schmude, Roy, Trojak, et al. 2024. “Prithvi WxC: Foundation Model for Weather and Climate.”
Szwarcman, Roy, Fraccaro, et al. 2025. “Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications.”
Vatsavai. 2024. “Geospatial Foundation Models: Recent Advances and Applications.” In Proceedings of the 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data.
Wei, Bora, Oommen, et al. 2025. “XAI4Extremes: An Interpretable Machine Learning Framework for Understanding Extreme-Weather Precursors Under Climate Change.”
Willig, Zečević, Dhami, et al. 2022. “Can Foundation Models Talk Causality?”
Zeraatkar, Faroughi, and Tešić. 2025. “ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models.”
Zhu, Cheng, Zhang, et al. 2019. “An Empirical Study of Spatial Attention Mechanisms in Deep Networks.” In.