Abstract
Sidewalk width is an important indicator of pedestrian accessibility, comfort, and network quality, yet large-scale width data remain scarce in most cities. Existing approaches typically rely on costly field surveys, high-resolution overhead imagery, or simplified geometric assumptions that limit scalability or introduce systematic error. To address this gap, we present UrbanVGGT, a measurement pipeline for estimating metric sidewalk width from a single street-view image. The method combines semantic segmentation, feed-forward 3D reconstruction, adaptive ground-plane fitting, camera-height-based scale calibration, and directional width measurement on the recovered plane. On a ground-truth benchmark from Washington, D.C., UrbanVGGT achieves a mean absolute error of 0.252 m, with 95.5% of estimates within 0.50 m of the reference width. Ablation experiments show that metric scale calibration is the most critical component, and controlled comparisons with alternative geometry backbones support the effectiveness of the overall design. As a feasibility demonstration, we further apply the pipeline to three cities and generate SV-SideWidth, a prototype sidewalk-width dataset covering 527 OpenStreetMap street segments. The results indicate that street-view imagery can support scalable generation of candidate sidewalk-width attributes, while broader cross-city validation and local ground-truth auditing remain necessary before deployment as authoritative planning data.
Abstract (translated)
人行道宽度是衡量行人可达性、舒适度及路网质量的重要指标，但大多数城市仍缺乏大规模宽度数据。现有方法通常依赖成本高昂的实地勘测、高分辨率航拍影像，或基于简化几何假设的估算，这些方法要么难以规模化，要么会引入系统性误差。为填补这一空白，本研究提出 UrbanVGGT——一个从单张街景图像估算指标人行道宽度的测量流程。该方法融合了语义分割、前馈式三维重建、自适应地平面拟合、基于相机高度的尺度标定，以及在恢复平面上的方向性宽度测量。在华盛顿特区的真实值基准测试中，UrbanVGGT 的平均绝对误差为 0.252 米，95.5% 的估算值与参考宽度的偏差在 0.50 米以内。消融实验表明，指标尺度标定是最关键组件；与替代几何主干网络的受控对比也验证了整体设计的有效性。作为可行性演示，我们进一步将该流程应用于三座城市，生成了覆盖 527 个 OpenStreetMap 街道路段的原型数据集 SV-SideWidth。结果表明，街景影像能够支持可扩展的人行道宽度候选属性生成，但在将其作为权威规划数据部署前，仍需进行更广泛的多城市验证和本地真实值核查。
URL
https://arxiv.org/abs/2603.22531
