An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
Chen Wu, Yinong Guo, Haonan Guo, Jingwen Yuan, Lixiang Ru, Hongruixuan, Chen, Bo Du, Liangpei Zhang

TL;DR
This study uses GF-2 satellite images and deep learning to analyze traffic density changes in Wuhan during COVID-19 lockdown, showing an over 80% reduction and subsequent recovery post-lockdown.
Contribution
It introduces a novel remote sensing approach combining high-resolution imagery and deep learning for intracity traffic analysis during a pandemic.
Findings
Traffic density dropped over 80% during lockdown.
Traffic recovered to normal levels after lockdown.
Deep learning vehicle detection achieved 62.56% accuracy.
Abstract
In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map…
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Taxonomy
TopicsImpact of Light on Environment and Health · COVID-19 epidemiological studies · COVID-19 impact on air quality
MethodsEmirates Airlines Office in Dubai
