Decades of lunar observations have given scientists an enormous amount of data about the Moon. Turning those maps and images into usable discoveries remains the harder part.
IBM and NASA have released an open-source artificial intelligence model designed to help researchers map craters and volcanic formations and predict areas with high potential for lunar ice. The NASA-IBM Lunar Foundation Model processes observations collected by multiple instruments at different resolutions, giving scientists a shared starting point for specialized lunar-research tools.
For developers and research organizations, the release provides a pretrained model and machine-learning-ready dataset that could reduce the work required to assemble data and train a lunar-mapping system from scratch.
AI model combines decades of lunar observations #
Sensors and instruments have generated petabytes of information about the Moon. Researchers often examine those materials manually or use machine-learning models developed for individual tasks, according to IBM’s announcement.
The foundation model was trained to identify relationships across different types and resolutions of lunar data. Researchers can adapt it to particular scientific questions instead of developing a separate system for every project.
Google has adopted a similar model-driven approach to scientific data with WeatherNext 3, which combines satellite and ground-station observations to generate hourly weather forecasts.
“NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job,” Kevin Murphy, NASA’s chief science data officer and acting chief data and AI officer, said in the announcement.
IBM and NASA also released what they describe as the first open-source lunar dataset of its kind to combine multimodal and multiresolution observations in a machine-learning-ready framework. The project reflects a broader push among technology companies and research institutions to develop open AI models for scientific work.
The dataset contains more than 30 spatially aligned layers from nine instruments across four missions. Its sources include NASA’s Lunar Reconnaissance Orbiter, Gravity Recovery and Interior Laboratory and Lunar Prospector missions, along with observations from the Japan Aerospace Exploration Agency’s SELENE mission, also known as Kaguya.
Model targets ice, craters and volcanic features #
One potential application is identifying areas where ice may exist beneath the lunar surface. Permanently shadowed regions are difficult to observe, but lunar ice could provide water and oxygen for future bases. It could also potentially be used to produce rocket fuel for missions traveling farther into space.
In a technical paper authored by IBM and NASA researchers, the model reduced root mean square error by as much as 22% compared with the SwinV2-B image model when predicting areas with high potential for lunar ice.
The system was also tested on Irregular Mare Patches, unusual formations that scientists study to understand the Moon’s volcanic and thermal history. When working with imperfect labels, the model reportedly improved the identification of the formations’ extent by 3% compared with SwinV2-B.
Crater detection represents another possible use. At meter-scale resolution, the model reportedly delivered accuracy comparable to current specialized methods while offering greater efficiency and lower fine-tuning costs.
At a broader contextual resolution of approximately 100 meters, it outperformed SwinV2-B by nearly 19% while using half as much training data, according to the paper.
Mapping craters can help scientists estimate the age and composition of lunar terrain. It can also help mission planners identify slopes, boulders and other hazards as NASA prepares for future surface missions following the Artemis II crewed flight around the Moon.
The results come from research conducted by the organizations behind the model. Independent researchers will need to test it across additional datasets and use cases before its broader performance becomes clear.
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What the release means for developers #
The Lunar Foundation Model joins IBM’s Prithvi family of open models, which covers weather, geospatial analysis and heliophysics. The aim is to provide reusable AI foundations that researchers can fine-tune for individual scientific tasks.
Research teams can now begin with a model trained on a large collection of aligned lunar observations instead of assembling every data source independently. Open access also allows developers to inspect the system, reproduce its reported results and create tools using the accompanying dataset.
Developers and research teams should first determine whether the model’s training coverage, available data types and spatial resolution match their intended task. Its outputs should then be compared with established methods and direct observations, particularly before they are used to evaluate ice potential, landing conditions or geological features.
The release could lower the technical and computing barriers to building specialized lunar-analysis tools, but its broader value will depend on whether independent researchers can reproduce the reported results. For now, it should be treated as a research foundation rather than a replacement for scientific observations or mission-specific validation.
Read more: Nvidia’s free PAIR tool shows how open-source software can distribute AI workloads across existing PCs and Macs, offering another example of developers gaining new ways to build and run AI systems.
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