First On-Orbit Demonstration of a Geospatial Foundation Model

2025-11-30

AI Summary

This work addresses the challenge of deploying large geospatial foundation models (GeoFMs) on resource-constrained spaceborne hardware. We propose a compact vision transformer architecture integrating model compression, cross-domain adaptation, and multi-task collaborative optimization to significantly reduce computational and memory overhead while preserving generalization capability and accuracy across downstream Earth observation tasks. Our approach enables reliable on-orbit inference for the first time on the IMAGIN-e payload aboard the International Space Station, supporting five core tasks—including land-cover classification and cloud detection. Compared to baseline models, it achieves a 72% reduction in parameter count, a 65% decrease in inference latency, and less than 1.2% accuracy degradation. This work establishes a scalable, flight-verified paradigm for intelligent onboard processing in Earth observation missions.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Learning on the Edge & Model Compression
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Develops compact geospatial foundation models for space hardware
• Reduces model size while preserving Earth observation task performance
• Enables onboard AI inference in resource-constrained orbital environments

Innovation

• Compact Vision Transformer variants for geospatial tasks
• Model compression and domain adaptation reduce resource demands
• On-orbit inference demonstrated on International Space Station payload