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

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 applicabili

SUBMITTER: Ahn D 

PROVIDER: S-EPMC10603027 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature