A human-machine collaborative approach measures economic development using satellite imagery

Donghyun Ahn, Jeasurk Yang, Meeyoung Cha (), Hyunjoo Yang (), Jihee Kim, Sangyoon Park (), Sungwon Han, Eunji Lee, Susang Lee and Sungwon Park
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Donghyun Ahn: KAIST
Jeasurk Yang: National University of Singapore
Meeyoung Cha: KAIST
Hyunjoo Yang: Sogang University
Sangyoon Park: Hong Kong University of Science and Technology
Sungwon Han: KAIST
Eunji Lee: KAIST
Susang Lee: College of Business, KAIST
Sungwon Park: KAIST

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Machine learning approaches using satellite imagery are providing accessible ways to infer socioeconomic measures without visiting a region. However, many algorithms require integration of ground-truth data, while regional data are scarce or even absent in many countries. Here we present our human-machine collaborative model which predicts grid-level economic development using publicly available satellite imagery and lightweight subjective ranking annotation without any ground data. We applied the model to North Korea and produced fine-grained predictions of economic development for the nation where data is not readily available. Our model suggests substantial development in the country’s capital and areas with state-led development projects in recent years. We showed the broad applicability of our model by examining five of the least developed countries in Asia, covering 400,000 grids. Our method can both yield highly granular economic information on hard-to-visit and low-resource regions and can potentially guide sustainable development programs.

Date: 2023
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DOI: 10.1038/s41467-023-42122-8

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