  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
transportation 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 images with the
high resolution under 1m. Accordingly, we here provide a quantitative
investigation of the transportation density reduction before and after lockdown
was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York,
and London) around the world during the COVID-19 epidemic, which is
accomplished by extracting vehicles from the multi-temporal high-resolution
remote sensing 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 0.5m. Our results indicate that
transportation densities were reduced by an average of approximately 50% (and
as much as 75.96%) in these six cities following lockdown. The influences on
transportation density reduction rates are also highly correlated with policy
stringency, with an R^2 value exceeding 0.83. Even within a specific city, the
transportation density changes differed and tended to be distributed in
accordance with the city's land-use patterns. Considering that public
transportation was mostly reduced or even forbidden, our results indicate that
city lockdown policies are effective at limiting human transmission within
cities.
