LunarFM: A Shared Multimodal Representation of the Moon's Surface
LunarFM turns six lunar instruments into one shared machine-learning map of the Moon’s surface.
The paper says the model maps 18 channels of orbital observations from three missions into a common embedding space. The authors show that space being used for similarity search, few-shot resource mapping, mineral abundance regression, and geological unit classification. They also release a co-registered dataset covering 70°S to 70°N, a pretrained multimodal masked autoencoder, and 768-dimensional surface embeddings. ArXiv · AI/CL/LG's note
The paper says the model maps 18 channels of orbital observations from three missions into a common embedding space. The authors show that space being used for similarity search, few-shot resource mapping, mineral abundance regression, and geological unit classification. They also release a co-registered dataset covering 70°S to 70°N, a pretrained multimodal masked autoencoder, and 768-dimensional surface embeddings. ArXiv · AI/CL/LG's note
score 5