LunarFM：月球表面的共享多模态表示
LunarFM: A Shared Multimodal Representation of the Moon's Surface
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中文总结 AI 辅助
因月球探索需求，针对多源观测致月球表面分析碎片化问题，提出LunarFM多模态基础模型，融合多仪器观测学习通用表示，支持多种下游应用，还提供相关数据集、预训练模型等，助力月球表面高效分析。
中文摘要 AI 辅助
全球对月球探索的重新关注，因原位资源利用和人类在月球持续存在的前景，对月球表面精确大规模表征需求日增。虽已收集大量轨道遥感数据，但科学分析和资源测绘因多仪器观测异质性、稀疏标签及特定任务建模工作流程而碎片化。本文引入LunarFM，一种多模态基础模型，从多样轨道测量中学习月球表面通用表示。它融合三次月球任务中六种仪器的观测，将18个输入通道映射到共享嵌入空间。实验表明该嵌入空间支持多种下游应用，包括相似性搜索、少样本资源测绘、矿物丰度回归和地质单元分类等，实现高效科学调查和资源导向分析。还提供了机器学习可用数据集、预训练多模态掩码自动编码器及配套嵌入数据集。所有代码和数据可在指定网址获取。
英文摘要
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/
发表机构
- University of Cambridge（剑桥大学）
- German Aerospace Center (DLR)（德国航空航天中心）
- SPAIDER SPACE（斯派德太空公司）
- Mines Paris - PSL University（巴黎矿业学院 - 巴黎文理研究大学）
- University of Bern（伯尔尼大学）
- Imperial College London（伦敦帝国理工学院）
- European Space Resources Innovation Center (ESRIC)（欧洲空间资源创新中心）
- Universidad de Antioquia（安蒂奥基亚大学）
机构由 AI 辅助整理，请以论文原文为准。
