arXiv:2511.02831v2 Announce Type: replace

Abstract: The data for remote sensing is constantly acquired, and new data comes from a growing number and diversity of satellites, while the vast majority of labeled data comes from older satellites. As remote-sensing foundation models for Earth observation scale up, the cost of (re-)training to support new satellites grows too, so cross-band generalization across sensors and satellites is increasingly important. We introduce GeoCrossBench, an extension of the popular GeoBench benchmark with a new evaluation protocol for cross-band generalization across sensors and satellites: it tests standard in-distribution performance with the same bands for train and test, generalization to inputs with no intersection between train and test; and generalization to test inputs containing a superset of the training bands. We develop $\chi$ViT, a self-supervised extension of the band-agnostic ChannelViT, as a supporting baseline for cross-band generalization. We evaluate a representative set of remote-sensing-specific and general-purpose vision models, characterize current performance, and identify directions for improvement through 11,900 H100 GPU-hours of experiments. When averaging dataset-specific metric scores, DOFA leads the in-distribution setting (61.30), frozen Panopticon leads the no-overlap setting (22.75), and ImageNet-pretrained ViT-B leads both the superset setting (56.19) and the overall average across settings (45.27). While top rankings in each setting are close, we clearly see that all models suffer significant performance losses when evaluated on unseen bands. We will publicly release the code and datasets to support the development of more future-proof remote sensing models with stronger cross-band generalization.