视觉-语言模型（vision-language models）正日益被用于基于重复采集的街景图像测量城市变迁，但其纵向可靠性尚未得到充分理解。我们检验了当街道本身未经历显著再开发时，感知得分（perception score）可能发生多大程度的变化。基于来自美国五座城市435个谷歌街景（Google Street View）观测点的4,648组连续时段图像对，我们发现对同一条街道重新拍摄会使感知得分平均变化0.80分，相当于同一城市内两条不同街道之间感知得分差异的66.5%。重复调用模型几乎不引入变异，而图像重编码（image re-encoding）与提示词顺序变化（prompt-order changes）各自贡献约五分之一的街道间差异。六类描述散射、对比度、色彩、曝光、锐度与镜面反射的图像统计量几乎无法解释剩余的时段间变异。尚存一个约0.1分的小幅系统性漂移，且该漂移随两次拍摄时间间隔增大而增强，与再开发标签未记录的细微物理变化一致。受控实验进一步表明，当允许相机与图像属性变化时，采集条件可导致得分偏移，且此类偏移的方向取决于所用模型。在众包图像中，仅相机几何参数一项即导致模型在45%的完全相同场景图像对中报告存在物理变化；将两幅图像归一化至共同虚拟相机后，该比例降至7.5%。尽管在单个地点层面可靠性较差，但聚合分析仍能恢复出一致的再开发信号：被判定为已发生变化的街道普遍被评价为更富裕、维护更好、围合性更强、绿化更少。这些结果表明，视觉-语言模型对城市变迁的测量在数百组配对观测的尺度上具备可靠性。

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 almost no variation, while image re-encoding and prompt-order changes each account for about one fifth of the between-street difference. Six image statistics describing scattering, contrast, colour, exposure, sharpness and specularity explain almost none of the remaining epoch-to-epoch variation. A small systematic drift of about 0.1 points remains and increases with the interval between captures, consistent with minor physical changes not recorded by redevelopment labels. Controlled experiments further show that acquisition conditions can shift scores when camera and image properties are allowed to vary, and that the direction of these shifts depends on the model. In crowdsourced imagery, camera geometry alone causes a model to report physical change in 45% of identical-scene pairs; normalising both images to a common virtual camera reduces this rate to 7.5%. Despite poor reliability at the individual-location level, aggregation recovers a coherent redevelopment signal: changed streets are judged wealthier, better maintained, more enclosed and less green. These results show that vision-language measurement of urban change is reliable at the scale of hundreds of paired observations, but not at the scale of individual sample points.