Our method can both yield highly granular economic information on hard-to-visit and low-resource regions and can potentially guide sustainable development programs. We showed the broad applicability of our model by examining five of the least developed countries in Asia, covering 400,000 grids. Our model suggests substantial development in the country’s capital and areas with state-led development projects in recent years. We applied the model to North Korea and produced fine-grained predictions of economic development for the nation where data is not readily available.

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.

However, many algorithms require integration of ground-truth data, while regional data are scarce or even absent in many countries.

Machine learning approaches using satellite imagery are providing accessible ways to infer socioeconomic measures without visiting a region. Data needed to reproduce our findings, figures, and tables are also available with detailed descriptions in the same GitHub repository and Zenodo database.Ĭode to replicate the findings of this study is available at Data generated by this study such as siScore are available in the GitHub repository ( ) and the Zenodo database (10.5281/zenodo.7694909). The access can be obtained by either perceiving proper permission from the data providers or providing appropriate credit. These datasets are available under restricted access for third party rights. GUID: 55321B89-6639-4529-A8F3-B957523578BA Data Availability Statementĭata obtained from third parties and used in figures include daytime satellite imagery (Google Earth at Sentinel-2 of Copernicus at ), background tiled map (Openstreetmap at ), nightlight imagery (Earth Observation Group-Payne Institute for Public Policy at ), land use map of North Korea (Ministry of Environment, Republic of Korea, its access permission process is described at ), the digital map of North Korea (National Geographic Information Institute, Republic of Korea at ), and mining industry dataset (I-RENK at ).

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