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Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations
ISPRS International Journal of Geo-Information ( IF 2.8 ) Pub Date : 2021-07-09 , DOI: 10.3390/ijgi10070471
Jonathan Cinnamon , Lindi Jahiu

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.

中文翻译:

数据驱动城市研究中的全景街道级图像:应用、技术和实际考虑的全面全球回顾

2007 年谷歌街景的发布激发了几个新的全景街景图像平台,包括 Apple Look Around、Bing StreetSide、百度全景、腾讯街景、Naver 街景和 Yandex Panorama。全球不断增加的 360° 城市捕捉为数据驱动的城市研究提供了相当多的新机会。本文首次对使用街道级图像进行城市分析的五个研究领域进行了全面的、最先进的评论:建筑环境和土地利用;健康和福祉;自然环境;城市建模和人口监测;和地区质量和声誉。与遥感影像和传统城市数据源(无论是手动、自动、或应用机器学习数据提取技术。主要优势包括低成本、快速、高分辨率和大规模数据采集,通过远程存在增强安全性,以及以独特的行人/车辆视角分析城市的规模和视角。然而,一些限制是显而易见的,包括捕获属性信息的能力有限、时间分析的不可靠性、深度和距离分析的使用有限,以及企业作为图像数据看门人的角色。调查结果为那些有兴趣使用全景街道级图像进行城市研究的人提供了详细的见解。以及独特的行人/车辆视角,用于在城市体验的规模和角度分析城市。然而,一些限制是显而易见的,包括捕获属性信息的能力有限、时间分析的不可靠性、深度和距离分析的使用有限,以及企业作为图像数据看门人的角色。调查结果为那些有兴趣使用全景街道级图像进行城市研究的人提供了详细的见解。以及独特的行人/车辆视角,用于在城市体验的规模和角度分析城市。然而,一些限制是显而易见的,包括捕获属性信息的能力有限、时间分析的不可靠性、深度和距离分析的使用有限,以及企业作为图像数据看门人的角色。调查结果为那些有兴趣使用全景街道级图像进行城市研究的人提供了详细的见解。
更新日期:2021-07-09
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