First On-Orbit Demonstration of a Geospatial Foundation Model

• URL: http://arxiv.org/abs/2512.01181v1
• Date: Mon, 01 Dec 2025 01:43:03 GMT
• Title: First On-Orbit Demonstration of a Geospatial Foundation Model
• Authors: Andrew Du, Roberto Del Prete, Alejandro Mousist, Nick Manser, Fabrice Marre, Andrew Barton, Carl Seubert, Gabriele Meoni, Tat-Jun Chin,
• Abstract summary: Geospatial foundation models (GeoFMs) promise broad generalisation capacity for Earth observation (EO) tasks.<n>However, their large size poses a barrier to deployment on resource-constrained space hardware.<n>We present compact variants of a Vision Transformer (ViT)-based GeoFM that preserve downstream task performance while enabling onboard execution.
• Score: 43.951325672023835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geospatial foundation models (GeoFMs) promise broad generalisation capacity for Earth observation (EO) tasks, particularly under data-limited conditions. However, their large size poses a barrier to deployment on resource-constrained space hardware. To address this, we present compact variants of a Vision Transformer (ViT)-based GeoFM that preserve downstream task performance while enabling onboard execution. Evaluation across five downstream tasks and validation in two representative flight environments show that model compression and domain adaptation are critical to reducing size and resource demands while maintaining high performance under operational conditions. We further demonstrate reliable on-orbit inference with the IMAGIN-e payload aboard the International Space Station. These results establish a pathway from large GeoFMs to flight-ready, resource-efficient deployments, expanding the feasibility of onboard AI for EO missions.

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