Measuring economic activity from space: a case study using flying airplanes and COVID-19
It provides a remote sensing method for policymakers to monitor economic disruptions and recovery, though it is incremental as it applies existing detection techniques to a new context.
This work tackled measuring economic activity during COVID-19 lockdowns by detecting flying airplanes around Europe's busiest airports using satellite imagery, winning the RACE challenge and integrating into a dashboard for decision-making.
This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesized that disturbances in human behavior caused by major life-changing events leave signatures in satellite imagery that allows devising relevant image-based indicators to estimate their impacts and support decision-makers. We present a case study for the COVID-19 coronavirus outbreak, which imposed severe mobility restrictions and caused worldwide disruptions, using flying airplane detection around the 30 busiest airports in Europe to quantify and analyze the lockdown's effects and post-lockdown recovery. Our solution won the Rapid Action Coronavirus Earth observation (RACE) upscaling challenge, sponsored by the European Space Agency and the European Commission, and now integrates the RACE dashboard. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities. Code and CNN models are available at https://github.com/maups/covid19-custom-script-contest