Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston

TL;DR

This paper presents a cost-effective deep learning approach using satellite images to identify hidden multi-family households, significantly improving census accuracy and resource efficiency in Houston.

Contribution

It introduces a novel method combining satellite imagery and deep learning to detect hidden multi-family households, addressing census under-counting issues.

Findings

Discovered over 1800 undetected HMF households in one Houston zipcode.

Demonstrated high accuracy and efficiency of the proposed method.

Potential to enhance census data collection and resource allocation.

Abstract

The accuracy and completeness of population estimation would significantly impact the allocation of public resources. However, the current census paradigm experiences a non-negligible level of under-counting. Existing solutions to this problem by the Census Bureau is to increase canvassing efforts, which leads to expensive and inefficient usage of human resources. In this work, we argue that the existence of hidden multi-family households is a significant cause of under-counting. Accordingly, we introduce a low-cost but high-accuracy method that combines satellite imagery and deep learning technologies to identify hidden multi-family (HMF) households. With comprehensive knowledge of the HMF households, the efficiency and effectiveness of the decennial census could be vastly improved. An extensive experiment demonstrates that our approach can discover over 1800 undetected HMF in a single zipcode of the Houston area.