Street view images (SVIs) coupled with computer vision (CV) techniques have become powerful tools in the planning and related fields for measuring the built environment. However, this methodology is often challenging to be implemented due to challenges in capturing a comprehensive set of planning-relevant environmental attributes and ensuring adequate accuracy. The shortcomings arise primarily from the annotation policies of the existing benchmark datasets used to train CV models, which are not specifically tailored to fit urban planning needs. For example, CV models trained on these existing datasets can only capture a very limited subset of the environmental features included in walkability audit tools. To address this gap, this study develops a virtual reality (VR) based benchmark dataset specifically tailored for measuring walkability with CV models. Our aim is to demonstrate that combining VR-based data with the real-world dataset (i.e., ADE20K) improves performance in automated walkability audits. Specifically, we investigate whether VR-based data enables CV models to audit a broader range of walkability-related objects (i.e., comprehensiveness) and to assess objects with enhanced accuracy (i.e., accuracy). In result, the integrated model achieves a pixel accuracy (PA) of 0.964 and an intersection-over-union (IoU) of 0.679, compared to a pixel accuracy of 0.959 and an IoU of 0.605 for the real-only model. Additionally, a model trained solely on virtual data, incorporating classes absent from the original dataset (i.e., bollards), attains a PA of 0.979 and an IoU of 0.676. These findings allow planners to adapt CV and SVI techniques for more planning-relevant purposes, such as accurately and comprehensively measuring walkability.

中文翻译：

---

使用街景图像实现自动化和全面的步行性审核：利用虚拟现实增强语义分割


街景图像 （SVI） 与计算机视觉 （CV） 技术相结合，已成为规划和相关领域中用于衡量建筑环境的强大工具。然而，由于在捕获一组全面的规划相关环境属性和确保足够的准确性方面存在挑战，因此这种方法通常难以实施。缺点主要来自用于训练 CV 模型的现有基准数据集的注释策略，这些数据集并不是专门为满足城市规划需求而定制的。例如，在这些现有数据集上训练的 CV 模型只能捕获步行性审计工具中包含的非常有限的环境特征子集。为了解决这一差距，本研究开发了一个基于虚拟现实 （VR） 的基准数据集，专门用于使用 CV 模型测量步行能力。我们的目标是证明将基于 VR 的数据与真实数据集（即 ADE20K）相结合可以提高自动步行能力审计的性能。具体来说，我们调查了基于 VR 的数据是否使 CV 模型能够审计更广泛的与步行性相关的对象（即全面性）并以更高的准确性（即准确性）评估对象。结果，集成模型的像素精度 （PA） 为 0.964，交并比 （IoU） 为 0.679，而纯实模型的像素精度为 0.959，IoU 为 0.605。此外，仅基于虚拟数据训练的模型，包含原始数据集中不存在的类（即护柱），其 PA 为 0.979，IoU 为 0.676。这些发现使规划者能够调整 CV 和 SVI 技术以用于更与规划相关的目的，例如准确和全面地测量步行能力。