The revolution in geospatial data is transforming how we study the growth and development of cities. As improved satellite imagery becomes available, new remote-sensing methods and machine-learning approaches have been developed to convert terrestrial Earth-observation data into meaningful information about the nature and pace of change of urban landscapes and human settlements. Urban areas can be detected in satellite imagery using machine-learning approaches, which typically rely on reference ground-truth data that mark urban features, either for training or for validation. Reference data are fundamental not only for mapping and assessing cross-sectional urbanization across space, but also for classification of urbanization over time. However, because they are expensive to collect, large-scale reference datasets are scarce. We present a low-cost machine-learning approach for pixel-based image classification of built-up areas at a high-resolution and large scale. Our m ethodology relies on data infusion of nighttime and daytime remotely sensed data for automatic collection of ground truth data, which we use for supervised pixel-based image classification of built-up land cover. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30m resolution maps that characterize built-up land cover in three diverse countries: India, Mexico, and the U.S. Our approach highlights the usefulness of data fusion techniques for studying the built environment and has broad implications for identifying the drivers of urbanization.