We draw upon recent advances in computer vision algorithms and in the availability and resolution of geospatial big data to improve the way data are collected for social science research. High quality surveys require a list of dwellings from which to select a sample. Many studies undertake considerable investments of time and money to create a list of households, and this costly work is repeated by many studies each year in the US, leading to inefficiencies and redundancies across projects. Research has shown that the most common methods of housing unit listing underrepresent rural households. The unique characteristics of the rural population, combined with the continued use of enumeration methods that miss some households, can bias estimates. Our approach uses computer vision techniques to detect dwellings in satellite images. Satellite images have become increasingly available at high resolutions and reduced cost in the last few years. During the same time, advances in computer vision have improved the ability of computers to find and identifying objects. We trained an algorithm to detect housing units in Wake County, NC. The approach achieves 83% accuracy and does not require large amounts of training data. We will continue to refine the algorithm over the coming months and expand it to other areas.