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.
Details
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Author
Tiecke,Tobias G., Liu,Xianming, Zhang,Amy, Gros,Andreas, LI,NAN, Yetman,Gregory, Kilic,Talip, Murray,Siobhan, Blankespoor,Brian, Prydz,Espen Beer, Dang,Hai-Anh H.
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Document Date
2017/12/15
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Document Type
Working Paper
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Report Number
148052
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Volume No
1
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Total Volume(s)
1
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Country
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Region
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Disclosure Date
2020/04/28
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Disclosure Status
Disclosed
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Doc Name
Mapping the World Population One Building at a Time
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Keywords
population estimate; international earth science information network; built up area; input data; rural area; census data; satellite imagery; annual population growth rate; global scale; spatial distribution of population; traditional authority; neural network; integrated household survey; computer vision; census enumeration area; information processing system; household survey data; population data; machine learning; study of population; difference in population; Population and Poverty; change in population; high resolution imagery; estimation of equation; spatial resolution; housing census; world population; Population Projection; Population Density; population distribution; administrative level; pattern recognition; data quality
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Citation
Tiecke,Tobias G. Liu,Xianming Zhang,Amy Gros,Andreas LI,NAN Yetman,Gregory Kilic,Talip Murray,Siobhan Blankespoor,Brian Prydz,Espen Beer Dang,Hai-Anh H.
Mapping the World Population One Building at a Time (English). Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/439381588065763562/Mapping-the-World-Population-One-Building-at-a-Time