Microscale environmental components, such as street furniture, sidewalks, and green spaces, significantly enhance street quality when properly identified and managed. Traditional in-person audits are time-consuming, so virtual audits using streetscape images and computer vision have been explored as alternatives. However, these often lack a comprehensive range of microscale components and do not consider attributes like materials. This paper proposes an automatic virtual audit method that recognizes microscale component types and materials in streetscape images using panoptic segmentation and material classification of segmented images of detected components. By surveying components affecting pedestrian-perceived street quality to include as many essential components as possible, 33 types of microscale components, as well as materials of sidewalk pavement, architectural elements, and street furniture, were identified with an overall F1 score of 0.946, demonstrating significantly improved performance compared with previous studies. This approach helps enhance street quality by evaluating built environments through an automatic virtual audit.

中文翻译：

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使用具有全景分割和图像分类功能的街景图像对微观环境组件和材料进行虚拟审计


如果识别和管理得当，街道设施、人行道和绿地等微观环境组成部分可以显著提高街道质量。传统的面对面审计非常耗时，因此已探索使用街景图像和计算机视觉的虚拟审计作为替代方案。然而，这些通常缺乏全面的微尺度组件，并且不考虑材料等属性。本文提出了一种自动虚拟审计方法，该方法使用检测到的组件分割图像的全景分割和材料分类来识别街景图像中的微尺度组件类型和材料。通过调查影响行人感知街道质量的组成部分，以包括尽可能多的基本组成部分，确定了 33 种类型的微尺度组成部分，以及人行道路面、建筑元素和街道设施的材料，总体 F1 得分为 0.946，表明与以前的研究相比，性能显着提高。这种方法通过自动虚拟审计来评估建筑环境，从而帮助提高街道质量。