  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.
