街景图像的开源数据流水线：以COVID-19疫情期间社区流动性为例
Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic
AI总结：
本文开发了一个开源数据流水线，用于处理车载360度视频并生成街景图像，通过在COVID-19疫情期间对西雅图进行38个月的纵向调查验证其可行性，为行人流量时间分析提供了超越现有SVI数据的新方法和新见解。
摘要：
街景图像（SVI）是研究人员常用的宝贵数据来源。研究人员已使用SVI数据来估计行人流量、进行人口统计监测，并更好地理解城市场景中的建成环境与自然环境。然而，公开可用的SVI数据最常见的来源是Google Street View。Google Street View图像的采集频率较低，这使得时间分析具有挑战性，尤其是在人口密度较低的地区。我们的主要贡献是开发了一个开源数据流水线，用于处理由车载摄像头录制的360度视频。该视频数据用于生成SVI，随后可作为时间分析的输入。我们通过在美国华盛顿州西雅图市COVID-19疫情期间进行的一项为期38个月的纵向调查中收集SVI数据集，来演示该流水线的使用。我们通过对图像中行人流量的统计分析来验证流水线的输出。我们证实了文献中已知的结果，并提供了关于户外行人流量模式的新见解。本研究证明了为研究目的收集和使用SVI的可行性和价值，这超出了当前可用的SVI数据所能实现的范围。文中还讨论了数据流水线和案例研究的局限性及未来改进方向。
英文摘要：
Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for temporal analysis. We demonstrate the use of the pipeline by collecting a SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Limitations and future improvements on the data pipeline and case study are also discussed.
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