You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change
Vision-language models (VLMs) are increasingly used to measure urban change from repeated street-level imagery, and the results are often mapped for individual sample points. Such maps assume that a perception score changes only if the street does. We test this assumption with repeated Google Street View captures of unchanged streets in five US cities and with controlled experiments that hold the photograph, scene or camera fixed. Re-photographing an unchanged street shifts its perception score by two-thirds of the average difference between two streets in the same city. Much of this shift arises in the scoring and averages out over question orders; the rest comes from the photographs and...
| Authors | Kaizhen Tan |
|---|---|
| Primary category | cs.CV |
| Published | 1 September 2026 |
| Last updated at source | 5 October 2026 |
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