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<p>As simulation-informed design gains importance in addressing urban complexity, integrating urban imagery into interactive feedback and decision-making has become increasingly essential. However, this potential remains underused, as urban imagery is often treated as a supporting variable in urban research rather than a core layer of spatial intelligence, hindering informed strategies in city branding, resource allocation, and livability. This study develops a data-driven framework, Street View Search Engine, which integrates urban imagery analysis with interactive exploration to advance human-centered insights into urban visual form. Based on 81,478 street view imagery collected in Hong Kong, China, a dataset comprising 19 visual features was first constructed to represent urban visual information across three categories: physical, impression, and isovist. Subsequently, the machine learning algorithm self-organizing maps was employed to train the dataset, producing a visualized “data landscape” that re-organizes street views according to their visual similarities. Third, building on the data landscape, this study develops the Street View Search Engine framework to conduct three main tasks: define visual foundations, comprehend streetscape morphology, and evaluate regional visual schemes. These tasks combine general-use exploration with research-oriented analysis: a web-based platform was developed to support general-use exploration (http://47.113.226.77/project1/#/), while various data processing methods were employed to enable in-depth professional investigations. By transforming raw data into a visualizable, computable, and interactive urban imagery system, this study paves the way for evidence-based interventions, strategic resource allocation, and greater public engagement in urban planning.</p>

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Street view search engine: A data-driven framework for urban imagery analysis and exploration

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Lan Ma,
  • Xu Zhao,
  • Xiwen Zhang,
  • Mingzhen Lu,
  • Chao Xie

摘要

As simulation-informed design gains importance in addressing urban complexity, integrating urban imagery into interactive feedback and decision-making has become increasingly essential. However, this potential remains underused, as urban imagery is often treated as a supporting variable in urban research rather than a core layer of spatial intelligence, hindering informed strategies in city branding, resource allocation, and livability. This study develops a data-driven framework, Street View Search Engine, which integrates urban imagery analysis with interactive exploration to advance human-centered insights into urban visual form. Based on 81,478 street view imagery collected in Hong Kong, China, a dataset comprising 19 visual features was first constructed to represent urban visual information across three categories: physical, impression, and isovist. Subsequently, the machine learning algorithm self-organizing maps was employed to train the dataset, producing a visualized “data landscape” that re-organizes street views according to their visual similarities. Third, building on the data landscape, this study develops the Street View Search Engine framework to conduct three main tasks: define visual foundations, comprehend streetscape morphology, and evaluate regional visual schemes. These tasks combine general-use exploration with research-oriented analysis: a web-based platform was developed to support general-use exploration (http://47.113.226.77/project1/#/), while various data processing methods were employed to enable in-depth professional investigations. By transforming raw data into a visualizable, computable, and interactive urban imagery system, this study paves the way for evidence-based interventions, strategic resource allocation, and greater public engagement in urban planning.