  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.
