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

Author

Listed:
  • Donghyun Ahn

    (KAIST)

  • Jeasurk Yang

    (National University of Singapore)

  • Meeyoung Cha

    (KAIST
    Institute for Basic Science)

  • Hyunjoo Yang

    (Sogang University)

  • Jihee Kim

    (Institute for Basic Science
    College of Business, KAIST)

  • Sangyoon Park

    (Hong Kong University of Science and Technology)

  • Sungwon Han

    (KAIST)

  • Eunji Lee

    (KAIST)

  • Susang Lee

    (College of Business, KAIST)

  • Sungwon Park

    (KAIST)

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.

Suggested Citation

  • Donghyun Ahn & Jeasurk Yang & Meeyoung Cha & Hyunjoo Yang & Jihee Kim & Sangyoon Park & Sungwon Han & Eunji Lee & Susang Lee & Sungwon Park, 2023. "A human-machine collaborative approach measures economic development using satellite imagery," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42122-8
    DOI: 10.1038/s41467-023-42122-8
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    References listed on IDEAS

    as
    1. Lee, Yong Suk, 2018. "International isolation and regional inequality: Evidence from sanctions on North Korea," Journal of Urban Economics, Elsevier, vol. 103(C), pages 34-51.
    2. Fatehkia, Masoomali & Kashyap, Ridhi & Weber, Ingmar, 2018. "Using Facebook Ad Data to Track the Global Digital Gender Gap," SocArXiv rkvb3, Center for Open Science.
    3. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    4. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    5. Fatehkia, Masoomali & Kashyap, Ridhi & Weber, Ingmar, 2018. "Using Facebook ad data to track the global digital gender gap," World Development, Elsevier, vol. 107(C), pages 189-209.
    6. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
    7. Jihee Kim & Kyoochul Kim & Sangyoon Park & Chang Sun, 2023. "The Economic Costs of Trade Sanctions: Evidence from North Korea," CESifo Working Paper Series 10630, CESifo.
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