ABSTRACT Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design.

中文翻译：

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Street-Frontage-Net：使用深度卷积神经网络的城市图像分类

摘要 大规模量化城市设计的各个方面对于帮助深入了解有助于公共空间成功的城市设计元素至关重要。在这项研究中，我们进一步开发了 Street-Frontage-Net (SFN)，这是一种卷积神经网络 (CNN)，可以成功地评估街道正面的质量，无论是活跃的（包含门窗的正面）还是空白的（包含墙壁的正面） 、围栏和车库）。小规模研究表明，临街越活跃，街道就越活跃、越安全。然而，收集评估街道临街质量所需的城市级数据的成本很高。SFN 模型使用深度 CNN 对街道的正面进行分类。本研究通过五个实验扩展了先前的研究。我们在样本外测试集的正面质量分类方面发现了稳健的结果，该测试集的准确率高达 92.0%。我们还发现社区的活跃临街面与房价上涨有显着联系。最后，我们发现与空白的临街面相比，活跃的临街面与更多的风景相关。虽然需要进一步研究，但结果表明在地理信息提取和城市设计中使用深度学习方法具有巨大潜力。