IBM and NASA Release Open-Source AI Model to Support Lunar Exploration
IBM and NASA released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models for scientific exploration of the Moon. The model exceeds widely used methods by up to 23% in identifying key lunar surface features such as potential ice deposits, craters and volcanic formations, cutting error in spotting high-potential ice regions by up to 22% versus the SwinV2-B (ImageNet) benchmark. Alongside the model, the teams built the first open-source unified lunar dataset of its kind, aggregating over 30 spatially aligned layers from nine instruments across four missions, including NASA's LRO and GRAIL and JAXA's SELENE/Kaguya.
Key figures
- Dataset layers
- 30+
- Dataset missions
- 4
- Dataset instruments
- 9
- Feature identification gain
- up to 23% vs widely used methods
- Volcanic feature capture gain
- 3%
- Crater detection outperformance
- nearly 19% at ~100 meter resolution using half the training data
- Lunar ice rmse reduction vs swinv2b
- up to 22%
AI analysis
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