  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 work, 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 (https://github.com/maups/covid19-satellite-analysis).
