One Adapter, Every Resolution: Gated Low-Rank Adaptation for Remote Sensing VLMs

Remote sensing imagery spans ground sampling distances (GSDs) from centimeters to tens of meters, so both the visual evidence for a geographic concept and the questions it can support change with physical scale. Existing remote sensing vision-language models (RS-VLMs) either ignore GSD or encode it as a text token, applying one set of scale-agnostic parameters across this spectrum. We show that GSD is decodable from frozen visual features, but exploiting it requires scale-dependent adaptation. We therefore introduce ScaleEarth, which conditions low-rank adaptation on the continuous scalar $s={\log }_{10}(\mathrm{GSD})$. Its core module, CS-HLoRA, gates the rank dimensions of a single shared LoRA with sigmoid functions of $s$ whose thresholds are initialized at object, structure, and semantic scales; the gates route gradients by scale during training and select the active adaptation subspace at inference, with the backbone frozen. When metadata is unavailable, a heteroscedastic head, SSE-U, estimates $s$ with calibrated uncertainty and falls back to a default scale when uncertain. To align supervision with this mechanism, we build GeoScale-VQA, 1.5M QA pairs in which every sample carries a resolved GSD and newly generated questions are conditioned on the same $s$. With an 8B backbone, ScaleEarth achieves an average score of 59.4 on XLRS-Bench ($+5.2$ over the strongest RS specialist). On OmniEarth-Bench, it achieves an average score of 40.71 ($+7.45$ over the strongest open-source baseline). The largest gains occur on scale-sensitive sub-tasks. The 4-bit variant can be deployed on a single 40 GB GPU and retains an average score of 57.4 on XLRS-Bench, with substantially lower adaptation and deployment costs.