SOURCE-LINKED INTELLIGENCE

You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Vision-language models are increasingly used to measure urban change from repeated street-level imagery, but their longitudinal reliability is not well understood. We test how much a perception score can change when the street itself does not undergo substantial redevelopment. Using 4,648 consecutive-epoch image pairs from 435 Google Street View standpoints across five US cities, we find that re-photographing the same street changes a perception score by 0.80 points on average, equivalent to 66.5% of the difference between two different streets in the same city. Repeated model calls contribute

Read original source ↗ Open in workspace

recordType

paper

region

Global