SentinelGeo: Evaluating Geographic Grounding in Vision-Language Models from Satellite Imagery

Vision-language models encode substantial geographic knowledge, yet it remains unclear how reliably they can apply it to a new observation and state a location claim that can be checked. We introduce **SentinelGeo**, a benchmark that turns single-image overhead geolocation into a falsifiable test of geographic grounding. It pairs 11,210 globally distributed, coordinate-labeled Sentinel-2 observations with an image-only protocol in which a model sees one satellite image, with no metadata, retrieval candidates, or tools, and must return a latitude-longitude prediction together with the evidence it relies on. Unlike existing overhead benchmarks, SentinelGeo scores both, auditing each rationale against the image and the true region. Across 18 systems, predictions depart clearly from random but remain coarse, with the best model missing by a median of 1,151 km, while audited rationales score poorly for narrowing the search toward the target region. A calibrated open baseline shows that lightweight task adaptation improves coordinate accuracy, including on a geographically held-out region, without a comparable change in rationale scores. SentinelGeo thus separates what models can place from what they can justify. The dataset and evaluation code will be publicly released.