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As the COVID-19 epidemic began to worsen in the first months of 2020, stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. Although traffic density reduction inside the city was felt subjectively, there has thus far been no objective and quantitative study of its variation to reflect the intracity population flows and their corresponding relationship with lockdown policy stringency from the view of remote sensing (RS) images with the high resolution under 1 m. Accordingly, we here provide a quantitative investigation of the traffic density reduction before and after lockdown was implemented in six cities (Wuhan, Milan, Madrid, Paris, New York, and London) around the world heavily affected by the COVID-19 epidemic, which is accomplished by extracting vehicles from the multitemporal high-resolution RS images. A novel vehicle detection model combining unsupervised vehicle candidate extraction and deep learning identification was specifically proposed for the images with the resolution of 0.5 m. Our results indicate that traffic densities were reduced by an average of approximately 50% (and as much as 75.96%) in these six cities following lockdown. The influences on traffic density reduction rates are also highly correlated with policy stringency, with an <b>R</b><sup>2</sup> value exceeding 0.83. Even within a specific city, the traffic density changes differed and tended to be distributed in accordance with the city's land-use patterns. Considering that public transport was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.

ieee journal of selected topics in applied earth observations and remote sensing

Traffic Density Reduction Caused by City Lockdowns Across the World During the COVID-19 Epidemic: From the View of High-Resolution Remote Sensing Imagery