IBM is launching a new open source AI model to get NASA back to the Moon - and making petabytes of lunar data available to study

To the Moon (and beyond)

IBM and NASA have released one of the first open source AI models to support the next generation of lunar exploration. The NASA-IBM Lunar Foundation Model, available on Hugging Face, will allow researchers to analyze decades of lunar observation data and identify geological features critical to NASA's goal of building a sustained human presence on the Moon.

The model was trained by IBM and NASA researchers on a huge, multimodal NASA dataset, which will also be released alongside the model, providing wider access to advanced AI systems to push progress in lunar exploration.

Capabilities and Current Limitations

At its most obvious level, the model will allow researchers to study petabytes of data gathered on the Moon's surface for potentially hazardous locations such as ice deposits or craters. Currently, scientists often rely on manual analysis or low-resolution, task-specific AI models, which can be computationally demanding and often lack accuracy for detailed geographic analysis.

The new release means scientists can adapt a single foundation model to investigate a range of lunar geologic features instead of building a new AI model for every potential issue. The model has already proved useful, identifying craters and volcanic features far more accurately and significantly reducing errors in locating potential ice deposits.

IBM, which has worked with NASA for over five decades, including on the Apollo missions, believes the model could help future astronauts navigate safely and even find essential resources, as well as helping scientists better understand the Moon's geological history.

Dataset Release

The release of the dataset marks the first time a unified, publicly-available cache has been made available for machine learning. It brings together:

• Over 30 spatially aligned layers
• Nine instruments across four missions
• Tens of thousands of images and maps showing unique geophysical properties of the lunar surface

Data sources include NASA's Lunar Reconnaissance Orbiter (LRO) and NASA's GRAIL mission.

Scientific Significance

Identifying lunar ice deposits could be particularly vital, as the presence of both water and oxygen will be crucial to establishing a human base on the Moon and even creating rocket fuel for future Mars missions.

Scanning the Moon's volcanic features, known as Iregular Mare Patches, can allow scientists to better understand the Moon's volcanic history and thermal evolution, as well as helping identify potential sites for landing and other surface operations.

Studying the Moon's craters can offer a wealth of information on its history, including the age of different terrains, their geology, and even the chemical composition of the early lunar interior, as well as helping to identify safe landing sites without hazards such as steep slopes and boulders.

Expert Commentary

"Uncovering the mysteries of the Moon requires an ability to learn from an extraordinary volume of scientific data," said Juan Bernabe-Moreno, Director of IBM Research Europe, UK and Ireland. "The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on."