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Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic

Matthew Martell, Nick Terry, Ribhu Sengupta, Chris Salazar, Nicole A. Errett, Scott B. Miles, Joseph Wartman, Youngjun Choe

TL;DR

The use of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera is demonstrated by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic.

Abstract

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.

Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic

TL;DR

The use of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera is demonstrated by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic.

Abstract

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.
Paper Structure (21 sections, 1 equation, 4 figures, 2 tables)

This paper contains 21 sections, 1 equation, 4 figures, 2 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. Supporting information
  8. Acknowledgments

Figures (4)

  • Figure 1: Sample images from the pedestrian detection data pipeline. The left image is an original 360$^\circ$ image from a data collection run. The image on the right is the right-hand side of the original image after orthorectification and pedestrian detection (both sides of the image are processed separately). There are two pedestrians that were detected by the algorithm (in red bounding boxes).
  • Figure 2: Flowchart of the data processing pipeline. The parts of the flowchart in gray occur on NHERI DesignSafe-CI, while the right hand part in blue is done on the Frontera cluster.
  • Figure 3: Time series data of the total detections per image (solid blue line, left axis), and detections per image for the subset of detections sharing an image with at least 4 others (orange dashed line, right axis). As the survey dates are irregular, all dates are included in the figure. Please note that the axis for total detections per image does not start at 0. This was done purposefully to facilitate comparison between the trends of the two graphs.
  • Figure 4: A depiction of our own detections per image data (blue, dashed; right axis) against Google Community Mobility data (orange, solid; left axis). The pearson correlation between the two data sets is 0.387. The Google Community mobility data is aggregated at King County, WA, while our data covers a survey route within Seattle, which belongs to King County. As the dates of surveys were irregular (e.g., due to weather conditions), all dates are included in the figure.