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Urban Sensing
Urban Vitality
Street View Video

街景视频用于城市感知:测量城市活力的潜力

Y Gu, X Liang, Z Fan, Y Hou, F Biljecki
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摘要

城市活力是城市宜居性和吸引力的关键品质,其中街道层面的社会动态被视为核心表征。然而,现有的捕捉此类动态的方法(主要通过行人数量)依赖于静态图像、基于位置的代理数据或定制传感器。这些方法通常是不连续的,时空分辨率有限,成本高昂,且忽视了声音在塑造环境中的作用。为了解决这些差距,本研究探讨了使用街景视频(SVV)这一新颖、有前景但尚未充分利用的数据源来捕捉街道层面动态的可行性。利用广泛可用的众包城市游览视频作为SVV的代表形式,我们开发了一个自动化且可扩展的框架,通过提取文本和深度信息来实现SVV的高精度地理定位。此外,我们提出了一种视觉-听觉管道,将连续的行人活动和声音环境整合作为揭示街道层面社会动态的互补维度。以墨尔本为例,我们识别了892个高质量且可地理定位的视频,总时长超过542小时,地理定位准确率达到77%。我们生成的时空地图不仅重现了研究较多的行人数量模式,还涵盖了此前被忽视的方面,包括人类行为(行走、站立、坐着)、声音强度以及声音类型(语音、音乐、车辆)。相关性和象限分析进一步探讨了这些活力维度之间的关系,揭示了独特的街道特征,并突出了传统道路分类的局限性。总体而言,这些发现证明了SVV作为城市活力指标的多模态和可重复来源的潜力,为未来的城市感知应用提供了概念验证。

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Based on panel data from 283 prefectural-level cities in China … >  保存 <https://scholar.google.com/citations?hl=zh-CN&update_op=email_library_add&info=u1QyqNYVv8sJ&citsig=AHwi3ncAAAAAbLCmM_yudVDaP2Am8XJhBKUE2XY> > 查看所有推荐内容 <https://scholar.google.com/scholar?sciupd=2&hl=zh-CN> > > Google 学术搜索发送此邮件,是因为您关注了为您的个人学术档案 <https://scholar.google.com/citations?hl=zh-CN&user=JZ9U7ZIAAAAJ>推荐的新文章。 > > 取消快讯 <https://scholar.google.com/scholar_alerts?view_op=cancel_alert_options&email_for_op=yaoy%40cug.edu.cn&alert_id=YAp0Ef-vDFoJ&hl=zh-CN&citsig=AHwi3ncAAAAAauHnsxWK2zVo3tnVynI0sQsUNlo> 【联网核验的原文摘要】 Urban vitality is a key quality of the livability and attractiveness of a city, with street-level social dynamics recognized as a central representation. However, existing approaches to capturing such dynamics, primarily through pedestrian volume, rely on static imagery, location-based proxies, or custom sensors. Such methods are typically discontinuous, limited in spatio-temporal resolution, costly, and overlook the role of sound in shaping environments. To address these gaps, this study explores the feasibility of using Street View Video (SVV), a novel, promising, yet under-utilized data source, for capturing street-level dynamics. Leveraging widely available crowdsourced city tour videos as a representative form of SVV, we develop an automated and scalable framework for high-precision geo-localization of SVV by extracting textual and depth information. Further, we propose a visual–auditory pipeline that integrates continuous pedestrian activity and sound environments as complementary dimensions that reveal street-level social dynamics. Using Melbourne as a case study, we identified 892 high-quality and geolocatable videos totaling over 542 h and achieved an accuracy of 77% for geo-localization. We generated spatio-temporal maps that not only reproduced well-studied pedestrian volume patterns, but also previously neglected aspects, including human actions (walking, standing, sitting), sound intensity, and sound types (speech, music, vehicle). Correlation and quadrant analyses further explored the relationships among these vitality dimensions, revealing distinctive street characteristics and highlighting the limitations of conventional road classifications. Overall, these findings demonstrate the potential of SVV as a multimodal and reproducible source of urban vitality indicators, providing a proof-of-concept for future urban sensing applications.

结论

街景视频(SVV)具有作为城市活力指标的多模态和可重复数据源的潜力,能够补充传统方法,提供包括行人行为和声音环境在内的更全面的街道社会动态视角,并为未来城市感知应用提供了概念验证。

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