Publication: Mapping the World Population One Building at a Time
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2017-12-15
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2020-05-05
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High resolution datasets of population density which accurately map sparsely distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, the authors present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment.
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“Tiecke, Tobias G.; Liu, Xianming; Zhang, Amy; Gros, Andreas; Li, Nan; Yetman, Gregory; Kilic, Talip; Murray, Siobhan; Blankespoor, Brian; Prydz, Espen B.; Dang, Hai-Anh H.. 2017. Mapping the World Population One Building at a Time. © World Bank. http://hdl.handle.net/10986/33700 License: CC BY 3.0 IGO.”
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