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

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Title
A human-machine collaborative approach measures economic development using satellite imagery
Author(s)
Donghyun Ahn; Jeasurk Yang; Meeyoung Cha; Hyunjoo Yang; Jihee Kim; Sangyoon Park; Sungwon Han; Eunji Lee; Susang Lee; Sungwon Park
Publication Date
2023-10
Journal
Nature Communications, v.14, no.1
Publisher
Nature Research
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. © 2023, The Author(s).
URI
https://pr.ibs.re.kr/handle/8788114/14665
DOI
10.1038/s41467-023-42122-8
ISSN
2041-1723
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
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