LunarFM把三个月球任务的六种仪器数据整合成一个模型，能直接做资源识别和地质分类，开源数据集和代码。
LunarFM是一个多模态基础模型，整合了来自3次月球任务的6种仪器数据，将18个输入通道映射到共享嵌入空间。该模型支持相似性搜索、少样本资源映射、矿物丰度回归和地质单元分类等下游任务。研究提供了从南纬70°到北纬70°的机器学习就绪数据集，以及预训练的多模态掩码自编码器。模型输出768维的月球表面属性表示，代码和数据均已开源。
LunarFM: A Shared Multimodal Representation of the Moon's Surface
The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for accurate, large-scale characterization of the lunar surface. Although vast quantities of orbital remote-sensing data have been collected, scientific analysis and resource mapping remain fragmented by heterogeneous multiinstrument observations, sparse labels, and bespoke task-specific modelling workflows. Here we introduce LunarFM, a multimodal foundation model that learns a general representation of the lunar surface from diverse orbital measurements. LunarFM assimilates observations from six instruments across three lunar missions, mapping 18 input channels to a shared embedding space. We demonstrate that this embedding space supports a diverse range of downstream applications, including similarity search, few-shot resource mapping, mineral abundance regression, and geological unit classification, enabling efficient scientific investigation and resource-oriented analysis. We provide a machine-learning-ready dataset of co-registered multimodal observations spanning latitudes from 70°S to 70°N, a pretrained multimodal masked autoencoder, and a companion embedding dataset providing a joint 768-dimensional representation of lunar surface properties. All code and data are available at https://lunarfm.trillium.tech/