Panoramic street-level imagery in data-driven urban research: A comprehensive global review of applications, techniques, and practical considerations

Abstract

The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360◦ provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.