2026-03-05 カリフォルニア工科大学 (Caltech)
Data from 2019 shows A: official United Nations Human Development Index (HDI) at the country level; ;B: HDI data at the province level from a previous study; C: Municipal-level estimates of HDI produced by Hannah Druckenmiller and colleagues; and D: Grid-level estimates of HDI at the 0.1-degree scale (approximately 10 kilometers square) produced by Druckenmiller and colleagues. Gray in the grid-level estimates indicates land area believed to be unsettled.
＜関連情報＞
- https://www.caltech.edu/about/news/measuring-local-well-being-from-space
- https://www.nature.com/articles/s41467-026-68805-6
衛星画像と機械学習を用いた国連人間開発指数の世界規模の高解像度推定 Global high-resolution estimates of the UN Human Development Index using satellite imagery and machine learning
Luke Sherman,Jonathan Proctor,Hannah Druckenmiller,Heriberto Tapia & Solomon Hsiang
Nature Communications Published:17 February 2026
DOI:https://doi.org/10.1038/s41467-026-68805-6
Abstract
The United Nations Human Development Index, which incorporates income, education and health, is arguably the most widely used alternative to gross domestic product. However, official country-resolution estimates (N=191) limit its use. We build on recent advances in machine learning and satellite imagery to produce and distribute global estimates of the Human Development Index for municipalities (N=61,530) and a 0. 1° × 0. 1° grid (N=819,309). To construct these estimates, we develop and validate a generalizable downscaling technique based on satellite imagery that allows for training and prediction with observations of arbitrary size and shape. We show how our estimates can improve decision-making and that more than half of the global population was previously assigned to the incorrect Human Development Index quintile within each country due to aggregation bias. We publish the satellite features necessary to increase the spatial resolution of any other administrative data that is detectable via imagery.
