Paper Detail
Marc Girona-Mata, Jakob Gawlikowski, Sumit Goski, Gautier Bardi de Fourtou, Valentin T. Bickel, Ben Moseley, Abigail Calzada-Diaz, Sylvester Kaczmarek, Raúl Ramos-Pollán
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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@article{gironamata2026lunarfm,
title = {LunarFM: A Shared Multimodal Representation of the Moon's Surface},
author = {Marc Girona-Mata and Jakob Gawlikowski and Sumit Goski and Gautier Bardi de Fourtou and Valentin T. Bickel and Ben Moseley and Abigail Calzada-Diaz and Sylvester Kaczmarek and Raúl Ramos-Pollán},
year = {2026},
abstract = {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},
url = {https://arxiv.org/abs/2607.22408},
keywords = {cs.LG},
eprint = {2607.22408},
archiveprefix = {arXiv},
}
{}