Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19

Abstract

This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesize 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 impact 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 postlockdown 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 is integrated into the RACE dashboard. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities. Code, trained model, and data are available at https://github.com/maups/covid19-custom-script-contest.

Keywords

• CNN-based object detection
• COVID-19 pandemic
• human and economic activity assessment
• remote sensing