# LunarFM: A Shared Multimodal Representation of the Moon's Surface

arXiv: https://arxiv.org/abs/2607.22408 · HTML: https://mlsanity.com/paper/2607.22408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [MultiMAE: Multi-modal Multi-task Masked Autoencoders](https://mlsanity.com/paper/2204.01678/) (2022)
- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities](https://mlsanity.com/paper/2412.14123/) (2024)
- [Galileo: Learning Global & Local Features of Many Remote Sensing Modalities](https://mlsanity.com/paper/2502.09356/) (2025)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction](https://mlsanity.com/paper/2505.05644/) (2025)
- [MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications](https://mlsanity.com/paper/2604.02719/) (2026)
- [Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing](https://mlsanity.com/paper/2607.03644/) (2026)
- [LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration](https://mlsanity.com/paper/2603.24696/) (2026)

## Built on by (most cited first, 1 of 1)

- [Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing](https://mlsanity.com/paper/2609.13283/) (2026)

## Abstract (arXiv)

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 70degS to 70degN, 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/