Street view imagery has rapidly ascended as an important data source for geospatial data collection and urban analytics, deriving insights and supporting informed decisions. Such surge has been mainly catalysed by the proliferation of large-scale imagery platforms, advances in computer vision and machine learning, and availability of computing resources. We screened more than 600 recent papers to provide a comprehensive systematic review of the state of the art of how street-level imagery is currently used in studies pertaining to the built environment. The main findings are that: (i) street view imagery is now clearly an entrenched component of urban analytics and GIScience; (ii) most of the research relies on data from Google Street View; and (iii) it is used across myriads of domains with numerous applications – ranging from analysing vegetation and transportation to health and socio-economic studies. A notable trend is crowdsourced street view imagery, facilitated by services such as Mapillary and KartaView, in some cases furthering geographical coverage and temporal granularity, at a permissive licence.

街景图像已迅速成为地理空间数据收集和城市分析的重要数据源，可以获取洞察力并支持明智的决策。这种激增主要是由大规模图像平台的扩散、计算机视觉和机器学习的进步以及计算资源的可用性催化的。我们筛选了 600 多篇最近的论文，以全面系统地回顾当前街道图像在与建筑环境相关的研究中的使用情况。主要发现是：(i) 街景图像现在显然是城市分析和地理信息科学的一个根深蒂固的组成部分；(ii) 大部分研究依赖于来自谷歌街景的数据；(iii) 它被用于无数领域和众多应用——从分析植被和交通到健康和社会经济研究。一个显着的趋势是众包街景图像，在 Mapillary 和 KartaView 等服务的推动下，在某些情况下，在许可的情况下，可以进一步扩大地理覆盖范围和时间粒度。