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 @math 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.
@cite_19: High-resolution population density maps are a critical component for global development efforts, including service delivery, resource allocation, and disaster response. Traditional population density efforts are predominantly survey driven, which are laborious, prohibitively expensive, infrequently updated, and inaccurate -- especially in remote areas. Furthermore, these maps are developed on a regional-basis where the methods used vary region to region, hence introducing notable spatio-temporal heterogeneity and bias. The advent of global-scale satellite imagery provides us with an unprecedented opportunity to create inexpensive, accurate, homogeneous, and rapidly updated population maps. To fulfill this vision, we must overcome both infrastructure and methodological obstacles. We propose a convolutional neural network approach that addresses some of the methodological challenges, while employing a publicly available, albeit low resolution, remote sensed product. The method converts satellite images into population density estimates. To explore both the accuracy and generalizability of our approach, we train our neural network on Tanzanian imagery and test the model on Kenyan data. We show that our method is able to generalize to unseen data and we improve upon the current state of the art by 177 percent.
The most similar work to ours also uses CNNs to estimate population from satellite imagery @cite_19 . The motivation of this paper is similar to ours, as we both attempt to create high-resolution gridded population counts for use in planning applications. This paper estimates population in Kenya at a 8km @math resolution with a CNN trained on data from Tanzania at a @math satellite pixel resolution. The author's propose a way to use their CNN's output as a weighted surface for population disaggregation, and compare this method to other methods for disaggregating population counts in Kenya. Our work differs in several important ways. First, we focus on validating our model's predictions as raw population projections and do not consider using our model's prediction as a weighted surface for distributing population counts. If the population (or projected population) of an area is known a priori, then any population method can degrade into a weighting scheme. Secondly, we focus on interpreting the results of our model as a way of validating its ability to generalize. Thirdly, we apply our method to the entire US using census block derived training and testing data.