PFU: A Universal Interface between Earth Observation Data and Frozen Foundation Models

RGB foundation models discard measurements that do not fit their three-channel interface. We study a different reuse strategy: a trainable spectral frontend that maps Sentinel-2 bands to pseudo-RGB while leaving the deployed backbone unchanged. Under one matched protocol and three predetermined seeds, PFU-E improves over the RGB baseline by 3.482 micro-mAP points on BEN-MM and 3.644 accuracy points on So2Sat. A 15-parameter spectral projection is already stronger than RGB on BEN-MM, while capacity-matched and test-time B08 controls show that the spatial frontend uses aligned NIR rather than only additional parameters. Beyond classification, pretrained PFU-E reaches 41.935 mIoU on PASTIS-HD, 19.909 points above a bare linear probe and 3.351 above scratch PFU. Twelve-band PFU-D improves Cashew Plant segmentation with both LVD and SAT backbones, and PFU-I dual-path and S1+S2 experiments demonstrate that the interface extends to wider spectra and multimodal inputs.