Foundations for a trustworthy Spatio-Temporal Foundation Model (TruSTFM)

Contact person: Filippo Bianchi

Status: Project

TruSTFM will develop a reusable foundation model for Earth-observation and climate data. Its core idea is to learn jointly from heterogeneous spatio-temporal sources—including satellite imagery and weather fields on regular grids, irregular station networks, and event records—across different spatial resolutions and uneven observation times. The model will combine a common representation for grids and graphs with multi-scale and continuous-time modelling.

Current models typically specialise in one data geometry, spatial resolution, sensor, or task. They therefore transfer poorly to new regions and struggle with missing observations or irregular sampling. TruSTFM is novel in addressing these limitations within one model and coupling this with a standardised, uncertainty-aware evaluation framework. This will make claims about transfer, robustness, and “foundation” capabilities more transparent and comparable.

Expected outcomes include a trained model core with input modules for multiple data types; open software for data processing, training, and evaluation; reusable benchmarks and transfer protocols; documentation; scientific publications; and two demonstrators. The first will combine coarse weather forecasts, geographic information, and sparse station observations for regional climate prediction and downscaling. The second will use SAR/InSAR time series and contextual data for surface-change and hazard monitoring. These results could support weather and climate services, environmental monitoring, renewable-energy analytics, natural-hazard management, Earth-observation services, and research communities working with sparse or heterogeneous sensor data.

Collaboration with user partners is central to ensuring practical relevance. Partners can contribute operational datasets, representative use cases, domain requirements, expert-labelled events, and validation under realistic conditions such as sensor outages, missing modalities, changing station networks, and deployment in new regions. They could also help integrate and test project outputs within existing forecasting or monitoring workflows.