NASA and IBM release open-source Lunar Foundation Model trained on 17 years of LRO data
NASA and IBM Research, with academic partners, published the open-source NASA–IBM Lunar Foundation Model, a multimodal foundation model trained from scratch on SomBench — a co-registered corpus of nearly 2 million lunar tile bundles spanning 11 modalities and multiple missions including 17 years of LRO data. The pretrained model improves tasks such as polar ice prediction (up to ~22% error reduction) and coarse-scale crater detection (~19% improvement versus SwinV2-B), and is intended to let researchers adapt lunar observations to downstream tasks with far fewer labels.