Space AI: Leveraging Artificial Intelligence for Space to Improve Life on Earth
Ziyang Wang
TL;DR
Space AI defines a formal, interdisciplinary field at the intersection of artificial intelligence and space science/engineering, proposing a four-context taxonomy (AI on Earth, AI in Orbit, AI in Deep Space, AI for Multi-Planetary Life) to organize autonomous, trustworthy space systems. The paper surveys historical milestones in space and AI, traces AI evolution, and details concrete applications, from ground-based simulation and mission planning to autonomous rovers, satellite networks, and in-space manufacturing, while highlighting governance, safety, and regulatory gaps. It presents a comprehensive framework for education, standards development, and international collaboration, arguing that robust autonomy, verification, and human–AI collaboration are essential for future multi-planetary exploration and Earth benefits. The work emphasizes technical challenges (radiation-hardened AI, edge computing, swarm coordination), ethical/societal considerations, and governance needs, and articulates an ambitious vision where Space AI accelerates discovery, reduces costs, enables sustainable off-world habitats, and fosters global cooperation.
Abstract
Artificial Intelligence (AI) is transforming domains from healthcare and agriculture to finance and industry. As progress on Earth meets growing constraints, the next frontier is outer space, where AI can enable autonomous, resilient operations under extreme uncertainty and limited human oversight. This paper introduces Space AI as a unified interdisciplinary field at the intersection of artificial intelligence and space science and technology. We consolidate historical developments and contemporary progress, and propose a systematic framework that organises Space AI into four mission contexts: 1 AI on Earth, covering intelligent mission planning, spacecraft design optimisation, simulation, and ground-based data analytics; 2 AI in Orbit, focusing on satellite and station autonomy, space robotics, on-board/near-real-time data processing, communication optimisation, and orbital safety; (3) AI in Deep Space, enabling autonomous navigation, adaptive scientific discovery, resource mapping, and long-duration human-AI collaboration under communication constraints; and 4 AI for Multi-Planetary Life, supporting in-situ resource utilisation, habitat and infrastructure construction, life-support and ecological management, and resilient interplanetary networks. Ultimately, Space AI can accelerate humanity's capability to explore and operate in space, while translating advances in sensing, robotics, optimisation, and trustworthy AI into broad societal impact on Earth.