LunarFM: A Shared Multimodal Representation of the Moon's Surface
Merged summary
TL;DR - LunarFM is a multimodal foundation model that unifies heterogeneous lunar orbital measurements into a shared surface representation. It could streamline scientific analysis and resource mapping despite sparse labels and fragmented datasets.
- Integrates 18 channels from six instruments across three lunar missions.
- Uses a multimodal masked autoencoder to produce 768-dimensional embeddings.
- Supports similarity search, few-shot resource mapping, mineral abundance regression, and geological unit classification.
- Releases code, pretrained models, embeddings, and co-registered data covering 70°S to 70°N.
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LunarFM: A Shared Multimodal Representation of the Moon's Surface
TL;DR - LunarFM is a multimodal foundation model that unifies heterogeneous lunar orbital measurements into a shared surface representation. It could streamline scientific analysis and resource mapping despite sparse labels and fragmented datasets.
- Integrates 18 channels from six instruments across three lunar missions.
- Uses a multimodal masked autoencoder to produce 768-dimensional embeddings.
- Supports similarity search, few-shot resource mapping, mineral abundance regression, and geological unit classification.
- Releases code, pretrained models, embeddings, and co-registered data covering 70°S to 70°N.