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.

Bibliographic Details

Main Authors:	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.
Format:	Working Paper biblioteca
Language:	English
Published:	World Bank, Washington, DC 2017-12-15
Subjects:	POPULATION DENSITY, POPULATION ESTIMATE, SATELLITE IMAGERY, POPULATION DISTRIBUTION,
Online Access:	http://documents.worldbank.org/curated/en/439381588065763562/Mapping-the-World-Population-One-Building-at-a-Time https://hdl.handle.net/10986/33700
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