NASA 与 IBM 开源月球基础模型：把 17 年轨道器数据变成月球科学的地基

The NASA-IBM Lunar Foundation Model makes decades of lunar observation data usable for machine learning. It's especially strong at predicting ice deposits at the poles and detecting craters. "NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job," says Kevin Murphy, NASA's chief science data officer. The data also has to be easier for scientists to use, he adds. NASA and IBM Research, working with several academic institutions, have now released the NASA-IBM Lunar Foundation Model. The two organizations describe it as one of the first open-source foundation models for lunar science. Unlike task-specific algorithms, a foundation model is pretrained on large volumes of unlabeled data and can then be adapted to specific tasks with just a few labeled examples. The team sees this as the main benefit for lunar research, where observation data is plentiful but labels are scarce. Ad The team trained the model from scratch using SomBench, which they say is the largest co-registered multimodal lunar corpus to date. It contains nearly 2 million tile bundles across 11 modalities and two spatial scales. About 1 million high-resolution images come from the Narrow Angle Camera (NAC) at roughly 1 meter per pixel, while just under 964,000 multispectral images come from the Wide Angle Camera at 100 meters per pixel. Ad The bulk of the data comes from 17 years of observations by the Lunar Reconnaissance Orbiter (LRO). According to NASA, its data volume exceeds that of all other NASA planetary missions combined. Data from the GRAIL mission (gravity field), Lunar Prospector (hydrogen), and JAXA's Kaguya/SELENE probe (mineralogy) rounded out the collection. In total, the dataset brings together more than 30 spatially aligned data layers from nine instruments and four missions. To prevent leakage between training, validation, and test data, the corpus is split geographically by map zones rather than randomly distributing individual tiles. Ad The model is based on TerraMind , a multimodal Earth observation model, but the researchers trained it from scratch instead of fine-tuning it. For each tile, the model receives the imaging geometry as explicit context, including illumination angles, sun position, and tile extent. On the Moon, lighting geometry shapes how the surface looks far more than the surface's actual properties do. Rather than forcing the model to correct for this from raw pixels, the team simply feeds it that information. The model also learns from high-resolution and coarse imagery together in a single training run, capturing both fine details and large-scale context. A technique called FlexiViT lets the same trained model adapt to tasks with different image patch sizes without retraining. Ad The team tested the model on crater detection at 100-meter and 1-meter scales and on predicting polar ice deposits. It also had to segment Irregular Mare Patches (IMPs), young volcanic features that challenge established timelines of lunar cooling. Across all tasks, the pretrained model matched or beat both common baselines and an architecturally identical control model with random initialization, according to the technical report . Ad The biggest gains showed up in ice deposit prediction. Permanently shadowed polar regions stay cold enough to preserve water ice for billions of years and are considered a potential resource for water, oxygen, and rocket fuel. The model cut prediction error by up to 22 percent compared to the best baseline, SwinV2-B, according to IBM. For coarse-scale crater detection, the model beat SwinV2-B by nearly 19 percent, according to IBM. That number comes from training with only half the data, suggesting the model needs fewer labeled examples to perform well. Even the randomly initialized control model beat five of seven baselines on ice prediction without any lunar pretraining. According to the researchers, part of the advantage comes from how