Mapping the World Population One Building at a Time

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.

World Bank, Washington, DC

关键词: POPULATION DENSITY;    POPULATION ESTIMATE;    SATELLITE IMAGERY;    POPULATION DISTRIBUTION;   

RP-ID  :  148052

学科分类：社会科学、人文和艺术（综合）

来源: World Bank Open Knowledge Repository

【 摘 要 】

High resolution datasets of populationdensity which accurately map sparsely distributed humanpopulations do not exist at a global scale. Typically,population data is obtained using censuses and statisticalmodeling. More recently, methods using remotely-sensed datahave emerged, capable of effectively identifying urbanizedareas. Obtaining high accuracy in estimation of populationdistribution in rural areas remains a very challenging taskdue to the simultaneous requirements of sufficientsensitivity and resolution to detect very sparse populationsthrough remote sensing as well as reliable performance at aglobal scale. Here, the authors present a computer visionmethod based on machine learning to create population mapsfrom satellite imagery at a global scale, with a spatialsensitivity corresponding to individual buildings andsuitable for global deployment.