街景图像日益被用于推断城市属性，但仅凭预测准确率无法揭示照片相对于同一地点已有数据所贡献的增量信息。我们利用五种公共数据源中的七个属性以及三个视觉语言模型（VLM），将基于图像的预测与现有城市数据进行对比。评估针对相同的城市单元进行，分别使用图像、任务上下文、邻近观测数据和公共记录，并通过图像替换和冲突记录测试模型对数据源的依赖程度。对于道路损坏、路缘坡道和房价，现有城市数据的匹配度或表现优于仅使用图像的模型；而对于人口统计，邻近的官方统计数据几乎达到了最佳图像结果的水平。在建筑类型、建筑功能和低层建筑楼层数方面，图像提供了更具信息量的数据。对于楼层数，随着与最近标注建筑距离每增加一倍，图像的优势提升5.7个百分点；但对于屋顶线常超出画面范围的高层建筑，图像优势则下降。模型经常遵循存在冲突的记录。OpenFACADES 的楼层注释是基于 OpenStreetMap 的楼层值生成的，其高度相关的误差模式与仅使用图像重新运行的结果相反。因此，街景图像的价值取决于视觉可辨识性和本地数据覆盖情况。将图像与现有城市数据进行比较，可以指导图像采集工作，并明确衍生城市地图的数据来源。

Street-view imagery is increasingly used to infer urban attributes, but predictive accuracy alone does not reveal how much a photograph contributes beyond data already available for the same place. We compare image-based predictions with existing urban data across seven attributes from five public resources and three VLMs. The same urban units are evaluated using images, task context, nearby observations, and public records, while image replacements and conflicting records test source reliance. Existing urban data matched or exceeded image-only models for road damage, curb ramps, and house price, while neighbouring official statistics nearly matched the best image result for population. Images were more informative for building type, building function, and low-rise floor count. For floor count, image advantage increased by 5.7 percentage points per doubling of distance to the nearest labelled building and declined for tall buildings whose rooflines often fell outside the frame. Models frequently followed conflicting records. OpenFACADES floor annotations were generated with OpenStreetMap floor values and showed the opposite height-dependent error pattern from image-only reruns. Street-view image value therefore depends on visual legibility and local data coverage. Comparing images with existing urban data can guide image collection and clarify the provenance of derived urban maps.