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

TL;DR

This paper investigates how high-resolution aerial imagery combined with machine learning techniques can accurately estimate neighborhood socioeconomic indicators such as population density, income, and education levels across U.S. cities.

Contribution

It introduces a semi-supervised clustering method for socioeconomic estimation from imagery, reducing reliance on labeled data, and compares it with a supervised CNN approach.

Findings

Features from imagery can estimate neighborhood density with R^2 up to 0.81.

Supervised approach explains about half the variation in income and education.

Semi-supervised method provides a foundation for label-free socioeconomic estimation.

Abstract

Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help serve as windows into the state of life on the ground and help "fill in the gaps" where community information is sparse, with estimates at smaller geographic scales requiring higher resolution sensors. Concurrent with improved sensor resolutions, recent advancements in machine learning and computer vision have made it possible to quickly extract features from and detect patterns in image data, in the process correlating these features with other information. In this work, we explore how well two approaches, a supervised convolutional neural network and semi-supervised clustering based on bag-of-visual-words, estimate population density, median household income, and educational attainment of individual neighborhoods from publicly available high-resolution imagery of cities throughout the United States. Results and analyses indicate that features extracted from the imagery can accurately estimate the density (R$^2$ up to 0.81) of neighborhoods, with the supervised approach able to explain about half the variation in a population's income and education. In addition to the presented approaches serving as a basis for further geographic generalization, the novel semi-supervised approach provides a foundation for future work seeking to estimate fine-scale information from aerial imagery without the need for label data.