This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas based on medium-resolution (10–30m/pixel) satellite imagery. The system and maps deploy the LULC taxonomy of the Atlas of Urban Expansion—2016 Edition: open, nonresidential, roads, and four types of residential space. We used supervised machine learning techniques to apply this taxonomy at scale. Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for policy, planning, and research. We demonstrate the training and application of machine-learning-based algorithms to characterize LULC over a large spatial and temporal range in a way that avoids many of the onerous constraints and expenses of the traditional LULC mapping process: manual identification and classification of features.