构建月球表面统一多模态表征，助力资源勘探与地质分析
LunarFM: A Shared Multimodal Representation of the Moon's Surface
- 融合六种仪器、三类任务的月球遥感数据，学习共享表征空间
- 支持少样本资源制图、矿物含量回归等10余项下游任务，准确率超基准模型
- 适合行星科学、资源勘探与遥感建模研究者使用
新一轮全球月球探索热潮推动了就地资源利用和长期人类驻留的愿景，对月球表面高精度、大规模表征的需求日益增长。尽管已有大量轨道遥感数据积累，但科学分析与资源制图仍受限于多源异构观测、标签稀疏及任务定制化建模流程。本文提出LunarFM，一种多模态基础模型，从多样化轨道测量中学习月球表面的通用表征。该模型整合来自三个月球任务的六种仪器数据，将18个输入通道映射至共享嵌入空间。实验表明，该嵌入空间可支持相似性检索、少样本资源制图、矿物丰度回归和地质单元分类等多种下游应用，显著提升科研效率与资源导向分析能力。我们公开了覆盖南北纬70°范围的共注册多模态观测数据集、预训练的多模态掩码自编码器及768维联合嵌入数据集。所有代码与数据均开放获取：https://lunarfm.trillium.tech/
原文摘要 · Abstract (English)
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/
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