Abstract:
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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 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 major cause for under-counting. Accordingly, we introduce a low-cost but high accuracy method that combine satellite imagery and deep learning technologies to identify all hidden multi-family (HMF) households. With comprehensive knowledge on the HMF households, the efficiency and effectiveness of the decennial census could be largely improved. An extensive experiment demonstrates that our approach can discover over 1800 undetected HMF in single zipcode of the Houston area.
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