IBM and NASA release open-source AI model for lunar exploration
New CapabilitiesThe Lunar Foundation Model maps ice, craters, and volcanic terrain using decades of data from four Moon missions
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Overview
Updated 27 minutes agoNASA is targeting 2028 for humans to walk on the Moon again. Before astronauts land, scientists must find ice in shadowed craters and identify safe terrain. On September 11, that mapping work got a new tool: an open-source AI model built by IBM and NASA.
The NASA-IBM Lunar Foundation Model is trained on more than 30 layers of data from nine instruments across four missions, including NASA's Lunar Reconnaissance Orbiter and Japan's SELENE/Kaguya spacecraft. It cuts error in identifying potential ice deposits by 22% versus a leading computer-vision baseline. The model and a co-registered lunar dataset are free to download from Hugging Face.
Why it matters
This shared lunar AI model gives scientists worldwide a free tool for mapping ice and craters, supporting NASA's 2028 crewed return.
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People Involved
Organizations Involved
Built the Lunar Foundation Model with NASA and released it open-source.
Provided lunar data and domain expertise; the model supports the Artemis program's 2028 crewed return.
Contributed SELENE/Kaguya mission data to the model's training set.
Platform hosting the open-source Lunar Foundation Model and dataset.
Timeline
August 2026 September 2026
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IBM and NASA release open-source Lunar Foundation Model
Today AnnouncementModel and co-registered lunar dataset published on Hugging Face. Ice detection error cut by 22% versus baseline.
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SpaceX Falcon 9 crashes into the Moon
IncidentThe rocket's upper stage created a new crater. IBM later used the image to validate the model's crater detection.
Historical Context
3 moments from history that rhyme with this story — and how they unfolded.
Apollo-era lunar mapping (1960s-1970s)
NASA scientists and cartographers mapped the Moon's surface manually using telescopic and orbital photography. The process produced charts that guided every Apollo landing, but it was painstaking and the maps had gaps, particularly at the poles.
Apollo missions landed successfully with the maps available at the time.
The lunar community spent decades filling map gaps with later missions like Lunar Prospector and the Lunar Reconnaissance Orbiter.
The Lunar Foundation Model automates this mapping tradition, covering the entire Moon with co-registered data from four missions in a single unified model.
DeepMind's AlphaFold open-source release (2021)
DeepMind released AlphaFold, an AI system that predicts protein structures from amino acid sequences, along with its source code and a database of over 350,000 predicted protein structures. Researchers worldwide adopted it for drug discovery, disease research, and more.
The scientific community embraced it rapidly, with thousands of researchers using it within months.
AlphaFold became the standard tool in structural biology, cited in tens of thousands of papers.
AlphaFold showed that an open-source AI model can become the shared analytical foundation of an entire scientific field, replacing bespoke task-specific systems.
IBM's Prithvi EO Earth observation model (2023)
IBM released Prithvi EO, an open-source geospatial foundation model trained on NASA's Harmonized Landsat-Sentinel data. The model can be fine-tuned for tasks like flood detection and crop classification without retraining from scratch. It was the first geospatial foundation model deployed in orbit.
It established IBM's pattern of building open, reusable scientific AI models and became the basis of the Prithvi family.
The Prithvi family now spans Earth observation, weather, heliophysics, and the Moon.
The Lunar Foundation Model is the direct descendant of Prithvi EO, using the same foundation-model approach and low-rank adapter fine-tuning techniques.
