arXiv:2607.03644v1 Announce Type: cross Abstract: Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition. Yet these datasets remain fragmented across archives, and no benchmark exists for evaluating machine learning on lunar data. We introduce Moonstone, the first multi-modal foundation model benchmark for lunar remote sensing. Our contributions are: (1) a 28-channel, 128 pixels-per-degree (~237 m) global lunar pretraining dataset from seven instrument families a
The proliferation of orbital lunar missions has accumulated vast datasets, creating an opportune moment for comprehensive AI model development and benchmarking.
This initiative establishes a foundational framework for AI agents to autonomously process and interpret complex lunar data, accelerating scientific discovery and resource identification.
Machine learning applications for lunar remote sensing will move from fragmented, ad-hoc analyses to standardized, multimodal foundation models, improving consistency and scalability.
- · Space agencies
- · Lunar resource extraction companies
- · AI/ML researchers in remote sensing
- · Deep space exploration programs
- · Manual data analysis service providers
- · Legacy, unimodal lunar data analysis techniques
The Moonstone model will enable more efficient mapping and characterization of lunar resources, including water ice and valuable minerals.
Improved lunar data analysis could lead to geopolitical competition for resource claims on the Moon, influencing international space policy.
The development of similar multimodal foundation models for other celestial bodies could accelerate multi-planetary resource exploration and off-world habitation efforts.
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Read at arXiv cs.AI