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Many areas of the world are without basic information on the socioeconomicwell-being of the residing population due to limitations in existing datacollection methods. Overhead images obtained remotely, such as from satelliteor aircraft, can help serve as windows into the state of life on the ground andhelp "fill in the gaps" where community information is sparse, with estimatesat smaller geographic scales requiring higher resolution sensors. Concurrentwith improved sensor resolutions, recent advancements in machine learning andcomputer vision have made it possible to quickly extract features from anddetect patterns in image data, in the process correlating these features withother information. In this work, we explore how well two approaches, asupervised convolutional neural network and semi-supervised clustering based onbag-of-visual-words, estimate population density, median household income, andeducational attainment of individual neighborhoods from publicly availablehigh-resolution imagery of cities throughout the United States. Results andanalyses indicate that features extracted from the imagery can accuratelyestimate the density (R$^2$ up to 0.81) of neighborhoods, with the supervisedapproach able to explain about half the variation in a population's income andeducation. In addition to the presented approaches serving as a basis forfurther geographic generalization, the novel semi-supervised approach providesa foundation for future work seeking to estimate fine-scale information fromaerial imagery without the need for label data.

Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators from High-Resolution Orthographic Imagery and Hybrid Learning