# UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images

Source: [UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images](<https://www.sota2.com/research/paper/69c36bf50636e5bf4afc63c8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-26T05:00:04.122Z

Authors: Kaizhen Tan, Fan Zhang

Published: 2026-03-23T19:52:18.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Sidewalk width estimation on Washington D.C.](<https://www.sota2.com/research/sota/sidewalk-width-estimation-on-washington-d-c.md>): Sidewalk width estimation; Washington D.C.; MAE: 0.252.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.22531>)

[PDF](<https://arxiv.org/pdf/2603.22531.pdf>)