Ahn Donghyun, Yang Jeasurk, Cha Meeyoung, Yang Hyunjoo, Kim Jihee, Park Sangyoon, Han Sungwon, Lee Eunji, Lee Susang, Park Sungwon
School of Computing, KAIST, Daejeon, 34141, Republic of Korea.
Department of Geography, National University of Singapore, Singapore, 117570, Singapore.
Nat Commun. 2023 Oct 26;14(1):6811. doi: 10.1038/s41467-023-42122-8.
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.
利用卫星图像的机器学习方法提供了无需实地考察就能推断社会经济指标的便捷途径。然而，许多算法需要整合地面真值数据，而在许多国家，区域数据稀缺甚至不存在。在此，我们展示了我们的人机协作模型，该模型使用公开可用的卫星图像和轻量级主观排名注释来预测网格级经济发展，无需任何地面数据。我们将该模型应用于朝鲜，并对该国经济发展进行了细粒度预测，该国的数据不易获取。我们的模型表明，近年来该国首都以及有国家主导发展项目的地区有显著发展。通过对亚洲五个最不发达国家的40万个网格进行考察，我们展示了我们模型的广泛适用性。我们的方法既能提供关于难以访问和资源匮乏地区的高度细化的经济信息，又能潜在地指导可持续发展项目。
