地理空间基础模型（GeoFM）有望为地球观测（EO）任务提供广泛的泛化能力，尤其在数据受限条件下表现突出。然而，其庞大的模型规模对资源受限的航天器硬件部署构成挑战。为此，我们提出了一种基于视觉Transformer（ViT）的GeoFM紧凑型变体，在保持下游任务性能的同时实现了机载执行。通过对五个下游任务的评估以及在两种典型飞行环境中的验证，表明模型压缩与领域适应对于降低模型尺寸和资源需求、同时在实际运行条件下维持高性能至关重要。我们进一步展示了在国际空间站搭载的IMAGIN-e有效载荷上实现的可靠在轨推理。这些结果确立了从大型GeoFM到可飞行、资源高效的部署路径，拓展了地球观测任务中机载人工智能的可行性。

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.