卫星图像
计算机科学
排名(信息检索)
数据科学
基本事实
社会经济发展
可持续发展
卫星
网格
网格单元
资源(消歧)
机器学习
人工智能
遥感
地理
经济增长
政治学
工程类
大地测量学
航空航天工程
法学
经济
计算机网络
作者
Donghyun Ahn,Jeasurk Yang,Meeyoung Cha,Hyunjoo Yang,Jihee Kim,Sangyoon Park,Sungwon Han,Eunji Lee,Susang Lee,Sungwon Park
标识
DOI:10.1038/s41467-023-42122-8
摘要
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.
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