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对于街景图像的开源数据管道:在COVID-19大流行期间对社区流动性的案例研究
Matthew Martell1, Nick Terry1, Ribhu Sengupta1
1Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America.
PloS one
|May 10, 2024
概括
研究人员开发了一个开源管道,从360度视频中创建街景图像 (SVI),用于纵向分析. 这种方法可以比目前的街景图像 (SVI) 更频繁地收集数据,用于城市环境研究.
科学领域:
- 城市规划和遥感技术
- 地理空间数据分析.
- 环境科学环境科学
背景情况:
- 街景图像 (SVI) 对于城市研究至关重要,包括行人数量估计和环境分析.
- 目前的SVI数据,主要来自谷歌街景,不经常收集,限制时间和纵向研究,特别是在人口较少的地区.
研究的目的:
- 开发一个开源数据管道,从360度视频生成街景图像 (SVI).
- 为了使城市环境的纵向分析能够更频繁地收集数据.
- 在COVID-19大流行期间创建一个新的数据集来研究室外行人交通模式.
主要方法:
- 开发了一个开源管道来处理360度车载视频数据到街景图像 (SVI).
- 在西雅图,华盛顿州,美国,收集了38个月的SVI纵向数据集.
- 在生成的图像中使用行人交通的统计分析验证了管道的输出.
主要成果:
- 管道成功生成了适合纵向分析的街景图像 (SVI).
- 对行人交通的统计分析证实了现有的文献发现,并揭示了新的模式.
- 该研究证明了使用定制收集的SVI数据进行研究的可行性.
结论:
- 开发的数据管道和数据集为纵向城市研究提供了宝贵的资源,克服了现有的街景图像 (SVI) 的局限性.
- 这种方法可以更频繁地收集数据,并对城市动态进行详细分析,例如行人交通.
- 这些方法和数据集代表了SVI用于研究目的的收集和应用的重大进步.
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