Abstract
The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.1.https://github.com/maups/covid19-satellite-analysis.
Highlights
THE COVID-19 outbreak is changing the world as never seen before
As part of this work, we describe an ensemble of convolutional neural networks (CNN) for simultaneous detection and classification of objects in highresolution aerial images
The xView dataset is very relevant to the COVID-19 problem because it used WorldView-3 as a source for more than 1,100 high-resolution images spanning about 800,000 aerial objects around the world, and covering a total area of 1,400 square kilometers
Summary
THE COVID-19 outbreak is changing the world as never seen before. The lockdowns and quarantines implemented worldwide can be noticed even from space. The European Union Commission requested the sharing of any satellite imagery related to the pandemic for research purposes.2 Such images will support decisions concerning: (1) traffic issues, to ensure citizens’ mobility but at the same time to avoid traffic jams that block the exchange of essential supplies; (2) medical infrastructure, to have knowledge about any temporary medical facility construction. Monitoring systems based on satellite images can support new indicators and new areas of interest with little effort, as all of them share the same database and input format. The xView dataset is very relevant to the COVID-19 problem because it used WorldView-3 as a source for more than 1,100 high-resolution images spanning about 800,000 aerial objects around the world, and covering a total area of 1,400 square kilometers. We show our framework in action on real examples of world scenes before and after the COVID-19 outbreak (Section 4.3)
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