Mapping the world population one building at a time

12/15/2017 ∙ by Tobias G. Tiecke, et al. ∙ Facebook 0

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, we 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. By combining this settlement data with census data, we create population maps with 30 meter resolution for 18 countries. We validate our method, and find that the building identification has an average precision and recall of 0.95 and 0.91, respectively and that the population estimates have a standard error of a factor 2 or less. Based on our data, we analyze 29 percent of the world population, and show that 99 percent lives within 36 km of the nearest urban cluster. The resulting high-resolution population datasets have applications in infrastructure planning, vaccination campaign planning, disaster response efforts and risk analysis such as high accuracy flood risk analysis.

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Acknowledgement

We thank D. Liu and T. Huang for insightful discussions on the denoising and feedback networks, and C. Deuskar and B. Stewart for insightful discussions on urbanization. All satellite imagery ©2016 DigitalGlobe. Funding for World Bank affiliates has been provided by The World Bank Living Standards Measurement Study ? Integrated Surveys on Agriculture (LSMS-ISA) project, and The World Bank Strategic Research Program Projects: (i) Use of Satellite Data and CDRs for Census-Independent Spatial Distribution of Populations and (ii) Survey Imputation for Improved Poverty and Shared Prosperity Diagnostics. Funding for CIESIN affiliates has been provided by Facebook. The boundary and census data collection were developed with funding from the National Aeronautics and Space Administration under Contract NNG13HQ04C for the Socioeconomic Data and Applications Distributed Active Archive Center (DAAC). Funding for Facebook affiliates was provided by Facebook, Inc.

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