Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-scale urban regions at high spatial resolutions has been difficult. We present a deep-learning-based multilabel classification model that can measure 22 subjective perceptions scores from street-view images. This model uses the results of a web questionnaire survey encompassing 22 subjective perceptions, with 8.8 million responses. Our model demonstrates high accuracy (0.80–0.91) in measuring subjective perception scores from street-view images and achieves low computational cost by training on 22 subjective perception relationships. The 22 subjective perceptions were analyzed using PCA and k-means analysis. By categorizing the 22 subjective perceptions into a two-dimensional space visualized and grouped into distinct groups—positive, negative, calm, and lively—we unearthed vital insights into the intricate nuances of human perception. In addition, the study used semantic segmentation to extract landscape elements from street-view images and applied ℓ1-regularized sparse modeling to identify the landscape elements structurally correlating with each subjective perception class. The analysis revealed that only seven out of nineteen landscape elements significantly correlated with subjective impressions, and these effects varied by class. Notably, sky coverage positively influences positive subjective perceptions, such as attractiveness and calmness, but negatively affects lively impressions. The proposed model can be used to map the overall image of a city and identify landscape design issues in community development design.

中文翻译：

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使用街景图像评估街景的主观感知

开发基于主观感知评估城市街景的模型对于定量理解非常重要。然而，以往的研究只考虑了有限类型的主观感知，忽略了它们之间的关系。此外，以低计算成本在高空间分辨率下准确测量大规模城市地区的主观感知一直很困难。我们提出了一种基于深度学习的多标签分类模型，可以测量街景图像的 22 个主观感知分数。该模型使用了包含 22 项主观看法、880 万份回复的网络问卷调查结果。我们的模型在测量街景图像的主观感知分数方面表现出很高的准确性（0.80-0.91），并通过对 22 个主观感知关系进行训练实现了较低的计算成本。使用 PCA 和 k 均值分析对 22 种主观感知进行了分析。通过将 22 种主观感知分类到可视化的二维空间中，并分为不同的组（积极的、消极的、平静的和活泼的），我们挖掘出了对人类感知错综复杂的细微差别的重要见解。此外，该研究使用语义分割从街景图像中提取景观元素，并应用 ℓ1 正则化稀疏建模来识别与每个主观感知类别结构相关的景观元素。分析显示，十九个景观元素中只有七个与主观印象显着相关，而且这些影响因类别而异。值得注意的是，天空覆盖对积极的主观感知（例如吸引力和平静）产生积极影响，但对生动的印象产生负面影响。所提出的模型可用于绘制城市的整体形象并确定社区发展设计中的景观设计问题。