  Knowing where people live is a fundamental component of many decision making
processes such as urban development, infectious disease containment, evacuation
planning, risk management, conservation planning, and more. While bottom-up,
survey driven censuses can provide a comprehensive view into the population
landscape of a country, they are expensive to realize, are infrequently
performed, and only provide population counts over broad areas. Population
disaggregation techniques and population projection methods individually
address these shortcomings, but also have shortcomings of their own. To jointly
answer the questions of "where do people live" and "how many people live
there," we propose a deep learning model for creating high-resolution
population estimations from satellite imagery. Specifically, we train
convolutional neural networks to predict population in the USA at a
$0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite
Landsat imagery. We validate these models in two ways: quantitatively, by
comparing our model's grid cell estimates aggregated at a county-level to
several US Census county-level population projections, and qualitatively, by
directly interpreting the model's predictions in terms of the satellite image
inputs. We find that aggregating our model's estimates gives comparable results
to the Census county-level population projections and that the predictions made
by our model can be directly interpreted, which give it advantages over
traditional population disaggregation methods. In general, our model is an
example of how machine learning techniques can be an effective tool for
extracting information from inherently unstructured, remotely sensed data to
provide effective solutions to social problems.
