Mapping the World Population One Building at a Time
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
Year of publication: |
2017
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Authors: | Blankespoor, Brian ; Dang, Hai-Anh H. ; Gros, Andreas ; Kilic, Talip ; Li, Nan ; Liu, Xianming ; Murray, Siobhan ; Prydz, Espen B. ; Tiecke, Tobias G. ; Yetman, Gregory ; Zhang, Amy |
Publisher: |
2017: World Bank, Washington, DC |
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