Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.

中文翻译：

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主观测量的街景感知为上海的城市设计策略提供信息

最近，出现了许多将计算机视觉 (CV) 应用于街景图像 (SVI) 数据集以客观提取各种街景特征（例如树木）的视图索引来代理城市场景质量的新研究。然而，人类感知（例如，可成像性）与使用视图索引无法完全捕获的视觉元素之间存在微妙的关系。相反，使用调查和访谈数据的主观测量更能解释人类行为。然而，关于将主观测量与 SVI 数据集相结合的有效性的讨论较少。为了解决这个问题，我们整合了众包、CV 和机器学习 (ML) 来主观衡量经典城市设计理论提出的四个重要感知。我们首先收集了专家对样本 SVI 的关于这四种品质的评级，这些品质成为了训练标签。CV 分割应用于 SVI 样本，提取街景视图指数作为解释变量。然后我们训练 ML 模型并在预测分数方面取得了很高的准确性。我们发现预测的复杂性分数与城市便利设施和服务兴趣点 (POI) 的密度之间存在很强的相关性，这验证了主观测量的有效性。此外，为了测试拟议框架的普遍性并为城市更新策略提供信息，我们将浦东的测量质量与世界知名的其他五个城市核心进行了比较。而不是使用卷积神经网络直接从通用图像特征预测感知分数，我们的方法遵循城市设计理论所建议和确认的影响多维人类感知的各种街景特征。因此，结果为政策制定者和城市规划者提供了更具可解释性和可操作性的意义。