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Open AccessArticle

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

1
Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
2
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
3
Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 208; https://doi.org/10.3390/rs13020208
Received: 9 December 2020 / Revised: 4 January 2021 / Accepted: 5 January 2021 / Published: 8 January 2021
The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level. View Full-Text
Keywords: COVID-19; social distancing; mobility pattern; remote sensing; vehicle detection COVID-19; social distancing; mobility pattern; remote sensing; vehicle detection
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MDPI and ACS Style

Chen, Y.; Qin, R.; Zhang, G.; Albanwan, H. Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images. Remote Sens. 2021, 13, 208. https://doi.org/10.3390/rs13020208

AMA Style

Chen Y, Qin R, Zhang G, Albanwan H. Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images. Remote Sensing. 2021; 13(2):208. https://doi.org/10.3390/rs13020208

Chicago/Turabian Style

Chen, Yulu; Qin, Rongjun; Zhang, Guixiang; Albanwan, Hessah. 2021. "Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images" Remote Sens. 13, no. 2: 208. https://doi.org/10.3390/rs13020208

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