This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: NASA is bringing artificial intelligence to the study of the moon, helping researchers transform how they analyze its surface. In an ongoing collaboration with IBM Research and several academic institutions, NASA has launched the NASA-IBM Lunar Foundation Model, one of the first open-source AI models built specifically for lunar science.
The model, trained primarily on data from NASA's Lunar Reconnaissance Orbiter (LRO), is hosted publicly on Hugging Face for anyone to use, with the complete codebase available on GitHub for testing and experimentation. The NASA-IBM Lunar Foundation Model supports the next generation of lunar science by helping researchers quickly analyze vast quantities of data to better understand the moon's surface. Using the model as a mapping tool, researchers can rapidly develop actionable strategies for evaluating the moon's rugged surface, understanding its geological past and planning future lunar research.
"NASA has spent decades building an extraordinary scientific record of the moon, but collecting data is only part of the job," said Kevin Murphy, chief science data officer and acting chief data and AI officer at NASA Headquarters in Washington. "We also have to make data easier for scientists to explore and use. The NASA-IBM Lunar Foundation Model shows what's possible when we bring AI to NASA's petabytes of scientific data.
That's a real opportunity we see with AI: turning large-scale data into new discoveries." Unlike traditional models that require building and training specialized algorithms from scratch for specific tasks, foundation models are pre-trained on vast, unlabeled datasets. The broad knowledge they acquire through pre-training allows them to generalize across multiple scientific domains through quick fine-tuning, making foundation models both versatile and efficient in accelerating scientific research. Data collected by NASA's LRO over the past 17 years were well-suited for training this foundation model because they cover most of the lunar surface in detail.
The data produced by the LRO mission are larger than those from all other NASA planetary missions combined, capturing an almost seamless, high-resolution mosaic of the entire moon. The NASA-IBM model was trained on roughly 2 million image tiles from this dataset, comprising more than 1 million high-resolution camera images at 1-meter resolution and nearly 964,000 multispectral images at 100-meter resolution. The model was also trained on high-resolution moon imagery and terrain data from multiple other missions, such as NASA's GRAIL (Gravity Recovery and Interior Laboratory), NASA's Lunar Prospector, and JAXA's (Japan Aerospace Exploration Agency) Selenological and Engineering Explorer.
Because the foundation model is already pre-trained on this dataset, planetary scientists can adapt the model to many different lunar research tasks, such as mapping craters, spotting young volcanic features and estimating where ice may exist near the lunar poles, by using only small amounts of labeled data. For researchers who study the moon's polar ice, the NASA-IBM model can help them estimate where ice patches are likely to be stable, on and below the surface. Dark areas like the moon's permanently shadowed regions remain cold enough to trap and preserve ice for up to billions of years.
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