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• W3037622648 abstract "Wuhan, the biggest city in China's central region with a population of more than 11 million, was shut down to control the COVID-19 epidemic on 23 January, 2020. Even though many researches have studied the travel restriction between cities and provinces, few studies focus on the transportation control inside the city, which may be due to the lack of the measurement of the transportation ban. Therefore, we evaluate the implementation of transportation ban policy inside the city by extracting motor vehicles on the road from two high-resolution remote sensing image sets before and after Wuhan lockdown. In order to detect vehicles from the remote sensing image datasets with the resolution of 0.8m accurately, we proposed a novel method combining anomaly detection, region grow and deep learning. The vehicle numbers in Wuhan dropped with a percentage of at least 63.31% caused by COVID-19. Considering fewer interferences, the dropping percentages of ring road and high-level road should be more representative with the value of 84.81% and 80.22%. The districts located in city center were more intensively affected by the transportation ban. Since the public transportations have been shut down, the significant reduction of motor vehicles indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city." @default.
• W3037622648 created "2020-07-02" @default.
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• W3037622648 date "2020-06-26" @default.
• W3037622648 modified "2023-09-27" @default.
• W3037622648 title "A Measurement of Transportation Ban inside Wuhan on the COVID-19 Epidemic by Vehicle Detection in Remote Sensing Imagery." @default.
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