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The accuracy and completeness of population estimation would significantlyimpact the allocation of public resources. However, the current census paradigmexperiences a non-negligible level of under-counting. Existing solutions tothis problem by the Census Bureau is to increase canvassing efforts, whichleads to expensive and inefficient usage of human resources. In this work, weargue that the existence of hidden multi-family households is a significantcause of under-counting. Accordingly, we introduce a low-cost but high-accuracymethod that combines satellite imagery and deep learning technologies toidentify hidden multi-family (HMF) households. With comprehensive knowledge ofthe HMF households, the efficiency and effectiveness of the decennial censuscould be vastly improved. An extensive experiment demonstrates that ourapproach can discover over 1800 undetected HMF in a single zipcode of theHouston area.

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