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An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2021-08-16 , DOI: 10.1016/j.jag.2021.102503
Chen Wu 1 , Yinong Guo 1 , Haonan Guo 1 , Jingwen Yuan 2 , Lixiang Ru 3 , Hongruixuan Chen 1 , Bo Du 3 , Liangpei Zhang 1
Affiliation  

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 was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.



中文翻译:


利用 GF-2 时间序列图像调查 COVID-19 疫情期间武汉市内的交通密度变化



为了减缓COVID-19的传播,武汉是2020年第一个实施严格封锁政策的城市。尽管大量研究讨论了城市和省份之间的旅行限制,但很少有研究关注城市内部交通控制的影响由于缺乏武汉的测量和可用数据。由于封城之初公共交通已经关闭,交通密度的变化是反映城内人口流动情况的一个很好的指标。因此,本文收集了高分二号卫星在武汉封城前、封城期间和封城后获取的分辨率为1 m的时间序列高分辨率遥感影像。提取道路上的车辆,进行交通密度统计,以反映武汉封城期间人传人的变化情况。利用Open Street Map获取观测路面,采用形态学滤波与深度学习相结合的车辆检测方法提取车辆,准确率达到62.56%。实验结果显示,封城期间,武汉市交通密度下降幅度超过80%,主要道路下降幅度甚至超过90%;解除封锁后,交通密度恢复至正常水平。整个研究区域的交通密度分布也呈现出明显的减少和增加。交通密度的大幅下降和恢复,表明武汉封城政策在控制城内人传人方面发挥了作用,解封后城市已恢复正常。

更新日期:2021-08-16
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