On-Orbit Execution of a Geospatial Foundation Model

Abstract

Geospatial foundation models (GeoFMs) offer broad generalisation across Earth observation (EO) tasks, particularly under data-limited conditions. However, their large model size and computational requirements remain major barriers to deployment on resource-constrained space hardware. To address this challenge, we develop compact variants of a GeoFM designed to preserve downstream task performance while enabling onboard execution. The models are evaluated across five downstream EO tasks and validated on two representative flight-computing environments. The results show that compression substantially reduces model size (1157.03 MB to 73.30 MB; approximately 16×) and enables execution on resource-constrained hardware (FP32-to-FP16 conversion for the Myriad-2 resulted in differences of no more than 0.21 percentage points across all reported task-level performance metrics), while task-head adaptation improves performance on mission-domain imagery under limited supervision. We further demonstrate on-orbit execution of the compact GeoFM using the IMAGIN-e payload aboard the International Space Station, where inference was performed on a preloaded curated dataset under orbital computing conditions. These findings establish a practical pathway for translating large pretrained GeoFMs into resource-efficient onboard implementations and expand the feasibility of onboard artificial intelligence for future EO missions.