NASA and IBM Release Open-Source AI Model to Accelerate Moon Explorations and Map Lunar Surface
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NASA and IBM Launch Open-Source AI Model for Future Moon Explorations - CNET
Anna Gragert (she/her/hers) was previously the lifestyle editor at HelloGiggles, the deputy editor at So Yummy and the senior lifestyle editor at Hunker.... Read full bio The NASA‑IBM Lunar Foundation Model is one of the first of its kind: a publicly available AI model created to support
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NASA, IBM launch new AI model for studying the moon
We've been studying the moon for a long time. NASA has gathered mountains of data over decades of lunar missions and studies. The data collected by the Lunar Reconnaissance Orbiter (LRO) alone, for example, exceeds that of all NASA's other planetary missions combined. Analyzing all this
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NASA and IBM open source lunar mapping tools
IBM and NASA have got together again and released an open source AI model of the Moon that could be used to make new discoveries about Earth's natural satellite. The NASA‑IBM Lunar Foundation Model has been trained on an extensive lunar observation dataset curated by researchers at the two
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IBM, NASA launch AI model to help map ice, craters on Moon
Sept 10 (Reuters) - IBM (IBM.N), opens new tab and NASA on Thursday released an open-source AI model designed to help scientists analyze decades of lunar observation data and support plans for a sustained human presence on the Moon. The NASA-IBM Lunar Foundation Model is a publicly available AI
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Moon's surface will be mapped in incredible detail using first-of-its-kind AI, NASA and IBM reveal
IBM and NASA have partnered to create a new artificial intelligence (AI) model capable of processing decades of lunar data so scientists can more accurately map the moon's surface one day. Decades of robotic lunar missions have left scientists with a massive, disjointed trove of data.
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NASA and IBM made an AI model for exploring the Moon - Engadget
This past spring, the world experienced a rare moment of collective joy and awe when NASA's Artemis II mission, the first crewed flight to the Moon since 1972, completed its historic lunar flyby. On April 6, astronauts Reid Wiseman, Christina Koch, Victor Glover and Jeremy Hansen flew farther from
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IBM and NASA release an open-source lunar foundation model
The release includes a unified lunar dataset built from nine instruments across four missions, and cuts error in identifying potential ice deposits by 23% against a general-purpose vision model IBM and NASA are releasing an AI model trained on decades of observations of the Moon, and making it
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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
IBM will help NASA researchers analyze decades of lunar observation data * IBM and NASA launch open source AI model to help further lunar research * Researchers will be able to better analyze petabytes of Moon data from last five decades * IBM and NASA also release dataset for public usage and
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NASA and IBM unveil an open source lunar AI model
The AI model could help scientists better identify lunar ice, volcanos and craters. A new open source AI model trained on NASA's extensive lunar observation data is expected to help scientists better navigate moon colonisation plans. The NASA‑IBM Lunar Foundation Model is one of the first
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NASA wants humans back on the Moon in 2028. IBM's new AI could help get them there
The Lunar Foundation Model will analyze decades of Moon data to help researchers find hazards, resources, and changes to the lunar surface. IBM is going to the Moon. Sort of. On Thursday, IBM announced the open-source release of the NASA-IBM Lunar Foundation Model, which may help scientists tap
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AI is going to help find the Moon's most valuable resource: How NASA and IBM are using AI to search for water ice
New artificial intelligence models are helping scientists find vital lunar water. This technology analyzes vast lunar data for future exploration efforts. Water ice is crucial for astronaut survival and rocket fuel production. AI also aids in identifying safe landing sites for lunar missions. This
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IBM, NASA launch AI model to help map ice, craters on Moon
The NASA-IBM Lunar Foundation Model is a publicly available AI tool designed to study the Moon. It was trained on more than 30 layers of data collected by nine instruments on four NASA missions, including the Lunar Reconnaissance Orbiter. IBM and NASA on Thursday released an open-source AI model
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IBM, NASA Release Open-Source AI Model Trained on Moon Data
International Business Machines and the National Aeronautics and Space Administration released an open-source artificial-intelligence model designed for research about the Moon. The open-source model is trained on extensive lunar observation data and is designed to "help scientists turn decades of
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IBM and NASA offer the Moon in an open-source AI model
IBM and NASA announce the open-source release of their NASA-IBM Lunar Foundation Model, one of the first publicly accessible artificial intelligence foundation models for the Moon's scientific exploration, now available. "Trained on a vast dataset of lunar observations curated by IBM and NASA
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IBM and NASA Release NASA-IBM Lunar Foundation Model for Scientific Exploration of the Moon
IBM and NASA announced the open-source release of the NASA-IBM Lunar Foundation Model, one of the first publicly available foundation models for scientific exploration of the Moon, now available. Trained on an extensive lunar observation dataset curated by IBM and NASA researchers, the model can
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NASA and IBM launched the NASA-IBM Lunar Foundation Model, a publicly available open-source AI model on Hugging Face. Trained on decades of lunar observation data from nine instruments across four missions, it helps researchers map ice and craters on Moon, identify volcanic features, and locate water ice deposits with up to 23% better accuracy than existing methods.
NASA and IBM Launch Open-Source AI Model for Moon Explorations
NASA and IBM released the NASA-IBM Lunar Foundation Model on Thursday, marking a significant advance in lunar research capabilities
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. This open-source AI model, now available on Hugging Face, was trained on decades of lunar observation data to support the Artemis program and future moon missions1
. The model integrates observations captured across different data formats, viewing angles, and spatial scales, enabling researchers to analyze the lunar surface at unprecedented depth without building AI systems from scratch each time3
.Processing Decades of Lunar Surface Data
Source: The Register
The model was trained on more than 30 spatially-aligned layers of data collected by nine instruments across four NASA missions, including the Lunar Reconnaissance Orbiter
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. NASA's Lunar Reconnaissance Orbiter alone has compiled data exceeding that of all other NASA planetary missions combined over 17 years2
. The compiled dataset, called SomBench, contains nearly 2 million overlapping map patches combining high-resolution optical cameras, laser altimeters, radar reflectance tools, and spectrometers measuring elemental density5
. Previously, scientists had to manually examine maps and images or rely on low-resolution machine learning models designed for specific tasks1
.Mapping Craters and Identifying Volcanic Features with Enhanced Accuracy
Source: Engadget
In benchmark tests, the NASA-IBM Lunar Foundation Model identified key features on the lunar surface up to 23% more accurately than widely used methods like SwinV2-B
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. For crater detection specifically, it outperformed SwinV2-B by nearly 19% using half as many training labels5
. The model excels at mapping craters to select safe landing sites and analyzing volcanic features called Irregular Mare Patches, which provide insight into the moon's thermal evolution3
. Campbell Watson, senior research manager at IBM Research, emphasized that making the model publicly available provides the global scientific community with a shared foundation that researchers can adapt to new questions about the moon1
.Locating Water Ice Deposits for Future Moon Missions
Source: Live Science
The model's ability to predict potential ice deposits represents a critical capability for the Artemis program, which plans to return astronauts to the Moon in 2028
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. Lunar ice indicates the presence of water and oxygen, resources considered essential for establishing a future Moon base and producing rocket fuel for missions to Mars4
. Ice deposits are found in permanently shadowed regions, among the most difficult areas to observe3
. The NASA-IBM model combines multimodal and multi-resolution observations to better predict where ice may be present on the lunar surface3
. When estimating polar ice prospectivity within the top meter of regolith, the model reduced errors in identifying areas with high potential for lunar ice by up to 22% compared with SwinV2-B5
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Technical Architecture Addresses Unique Lunar Challenges
The model employs masked-token learning, where parts of a dataset are hidden so the AI predicts concealed information across millions of examples
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. Lighting analysis is built directly into the core architecture, with explicit metadata describing solar angles and spacecraft positions supplied to avoid getting tricked by shadows5
. Processing lunar observations presents unique challenges because the moon lacks an atmosphere, creating extreme sunlight geometry with deep shadows and washed-out geological details5
. Most fine-tuning experiments were conducted using Nvidia A100 GPUs, though smaller-scale experiments and inference workloads may be possible on more modest hardware3
.Part of Broader NASA-IBM AI Collaboration
The NASA-IBM Lunar Foundation Model joins the Prithvi family of open foundation models, which span geospatial, weather, climate, and heliophysics applications
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. NASA and IBM have collaborated for over 60 years, since they first worked together to put the first man on the moon1
. "NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job," said Kevin Murphy, NASA's chief science data officer. "The NASA-IBM Lunar Foundation Model shows what's possible when we bring AI to NASA's petabytes of scientific data"2
. Juan Bernabe-Moreno, IBM director of research for Europe, noted 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"3
. This release reflects a larger trend in planetary science, where space agencies and private companies are exploring how AI models can increase scientific productivity, reduce costs, and streamline workflows2
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