GoKawiilNASA’s Apollo missions proved that humans could make it to the Moon. The Artemis program now underway will test whether we can live and work there, too. NASA’s long-term vision calls for building a base on the Moon where astronauts can carry out research, test new technologies, and prepare for a push onward to Mars.
A good map is handy when exploring any unfamiliar place, but when the terrain is as rugged and otherworldly as the Moon’s, it’s essential. Today, IBM and NASA are open-sourcing the most thorough model for mapping the Moon to date. It consolidates and harmonizes decades of data collected on US and Japanese missions flown over our closest celestial neighbor.
Called NASA-IBM Lunar Foundation Model, it’s the first AI model to integrate observations captured in a range of modalities, and at different viewing angles and spatial scales. By distilling these disparate measurements into one representation, researchers have created a model for the Moon that, like earlier models of the Earth and Sun, Prithvi EO and Surya, can be quickly adapted to different tasks and reused.
A lunar AI model has many potential uses, but NASA has initially prioritized three: mapping the smaller, uncatalogued craters that pockmark the Moon’s surface; investigating its volcanic history; and scouring craters at both poles for ice, which could provide future astronaut crews with a source of drinking water, oxygen, and fuel.
“Experienced travelers know to get the lay of the land before setting out for a foreign destination,” said Juan Bernabé-Moreno, director of IBM Research Europe for Ireland and UK. “We hope that our AI model can help the science community explore the lunar landscape and help the next generation of astronauts find their way around before heading into space.”
A Rosetta stone for multi-sensor data
Image courtesy of NASA.
The new model addresses a longstanding challenge for lunar scientists handling high-volume, multi-sensor data at varying scales. NASA’s Gravity Recovery and Interior Laboratory (GRAIL) mission, for example, mapped the Moon’s gravitational field at a scale of 20 kilometers-per-pixel to visualize its crust and subsurface while NASA’s Lunar Reconnaissance Orbiter (LRO) swooped in to hunt for ice nestled in the Moon’s dark polar craters and to image small boulders and crater rims at scales of 1 meter-per-pixel.
To align such a diverse collection of measurements, IBM and NASA looked for an AI architecture fluent in multiple scientific dialects. They went with a version of TerraMind, the Earth-observation model developed by IBM and the European Space Agency (ESA). TerraMind excels at integrating different data types and resolutions and learning cross-modal correlations to fill in missing or noisy values.
One area in which scientists will look to the new AI model for help is understanding the Moon’s difficult lighting conditions. A lunar ‘day’ consists of two weeks of sunlight followed by two weeks of darkness, which can dramatically change the appearance of the Moon’s surface features.
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