Measuring economic activity from space: a case study using flying airplanes and COVID-19

TL;DR

This paper presents a satellite imagery-based method to measure economic activity by detecting flying airplanes around major airports, demonstrating its effectiveness during COVID-19 lockdowns and recovery phases.

Contribution

It introduces a novel remote sensing approach using airplane detection in satellite images to estimate economic activity, validated through a COVID-19 case study.

Findings

Detected significant reductions in airplane activity during lockdowns.

Correlated airplane activity with economic recovery post-lockdown.

Won the RACE challenge for satellite-based COVID-19 impact analysis.

Abstract

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