Researchers can now use an open source AI model from IBM and NASA to sift through decades of lunar observations for ice, craters and volcanic terrain using the newly released NASA-IBM Lunar Foundation Model, in preparation to return people to the Moon’s surface. The system is available as a free download on Hugging Face, with its full codebase published on GitHub, according to NASA Science.
Studying the lunar surface has always been a relatively slow process with either of two possible options, which included combing through maps and images by hand, or training a narrow, low-resolution machine-learning model for each separate request. Both of these options are expensive to run and often miss the fine detail scientists need in their researches, IBM said in its announcement.
The release of a foundation model helps though, as researchers can then use a pre-trained model for every new task instead of building a fresh one when working with a different geologic feature. The lunar model is the latest addition to IBM’s Prithvi family of open science models, which already spans applications in geospatial analysis, weather and heliophysics. Juan Bernabe-Moreno, director of IBM Research Europe, UK and Ireland, said the new system gives scientists a way to analyze lunar observations at scale and identify patterns that may be difficult to detect when studying individual datasets.
“The model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation,” Bernabe-Moreno said.
Lunar foundation model edges performance benchmarks
NASA and IBM measured the model against SwinV2-B, a Microsoft-trained vision system widely used as a baseline for image analysis. The new model cut errors by 23% when used in locating ice deposits, and beat SwinV2-B by 19% in finding and classifying craters, even though it trained on half the data. IBM’s technical paper reported a 3% performance improvement when the model was used to identify volcanic features known as Irregular Mare Patches, while also requiring less fine-tuning.
The model was put to a live test on August 5, when IBM fed it an image showing the impact site left after a SpaceX Falcon 9 rocket struck the Moon. Despite the new impact appearing almost directly over an existing crater, the model correctly identified it as a newly formed crater.
The goal remains to make sustained human presence on the Moon possible. Mapping permanently shadowed regions near the lunar poles remains a major challenge because these areas are among the hardest on the Moon to observe and could contain deposits of subsurface ice. IBM said the ice could supply water and oxygen for future lunar bases while also providing raw materials for rocket fuel that could support missions to Mars.
The dataset may outlast the model
The organizations also released what they called the first unified, machine-learning-ready dataset of the Moon. The dataset brings together more than 30 spatially aligned layers collected by nine instruments across four separate lunar missions.
This dataset pulls imagery from NASA’s Lunar Reconnaissance Orbiter and GRAIL gravity mission and adds data from Japan’s SELENE/Kaguya orbiter. Bernabe-Moreno said that the roughly two million co-registered data points might prove to be the lasting contribution over the model itself. “The data is what really creates the industry of AI models,” he stated.
The AI model’s release builds on a partnership that dates back more than five decades to the Apollo program.
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This articles is written by : Nermeen Nabil Khear Abdelmalak
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