Decades of robotic lunar missions have left scientists with a massive, disjointed trove of data. Traditionally, this data has been parsed by limited transformer models such as SwinV2-B, created in 2022 as a general-purpose model to understand images and improve accuracy on photo recognition and related vision tasks. Spacecraft orbiting the moon captured this data using a mismatched array of sensors, without an accessible way to analyze or utilize it.
To crack the data bottleneck, researchers from NASA and IBM teamed up to build the Lunar Foundation Model (LFM). This agile AI system is designed to piece together multimodal, multiresolution data to build a detailed picture of the lunar surface to support future missions. The team published their findings Sept. 10 in a technical paper shared with Live Science.
Scientists have collected troves of data about the moon over many decades — but much of it is disjointed and difficult to analyze. (Image credit: IBM)For NASA, this includes thorny issues such as generating a reliable crater map so researchers can plot safe landing zones, or analyzing those craters for clues about the chemical makeup of the moon's interior and its history.
Processing lunar observations presents unique computational challenges that differ from those of similar models, which cover things like weather, geospatial data and heliophysics. NASA's Lunar Reconnaissance Orbiter and other spacecraft collect measurements across vastly different spatial scales, ranging from broad regional maps at a resolution of 100 meters per pixel down to terrain scans resolving at 1 m per pixel.
To overcome these problems, the team compiled a layered benchmark dataset named SomBench, made up of nearly 2 million overlapping map patches called tiles. SomBench organizes that data into aligned tracks so data from completely different instruments, or imagery taken at different resolutions or angles, all end up together as long as they're capturing the same tiles.
A visualization of the NASA-IBM Lunar Foundation Model’s ice prospectivity capability.IBMA visualization demonstrating the NASA-IBM Lunar Foundation Model’s craterdetection capability.IBMA visualization demonstrating the NASA-IBM Lunar Foundation Model’s ability to identify and map irregular mare patches (IMPs), rare volcanic features on the Moon’s surface.IBMAn infographic illustrating how the NASA-IBM Lunar Foundation Model assists withanalyzing lunar imagery and data at scale.IBM
LFM learns to interpret all that layered data through a technique called masked-token learning, which is where parts of a dataset are hidden so the AI has to fill in the blanks. During training, the AI is shown a portion of a lunar tile, like its visible light appearance and elevation, and nothing else. The model then continuously predicts the concealed information across millions of examples, and learns the relationships between elements like lighting, terrain structure and physical geography.
One giant leap
The AI model performed well across four tasks it was evaluated on. In crater detection, it outperformed SwinV2-B by nearly 19% using half as many training labels. When estimating polar ice prospectivity within the top meter of regolith (lunar dust and rocks), LFM maintained strong predictive power with fewer data channels and reduced errors in identifying areas with high potential for lunar ice by up to 22% compared with SwinV2-B. Its performance on meter-scale crater mapping and rare volcanic landforms was similarly competitive with top custom models.
LFM will be made available to researchers freely through the open-source AI repository Hugging Face so that teams can tailor it to their specific needs when studying the lunar surface. (Image credit: NASA)RELATED STORIES
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In the near term, scientists will likely use the LFM as a backbone for key lunar remote-sensing tasks, while the long process of fine-tuning it ramps up. Over time, the model will be refined for tasks like improved crater detection and mapping, segmenting subtle geomorphic units such as irregular mare (volcanic activity) patches, and improving polar ice detection by integrating terrain, illumination, and thermal layers.
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