We explored a one-class classifier, the support vector data description (SVDD), using the Suomi National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite and normalized difference vegetation index to map the urban extent, which was tested in the Beijing and Tianjin city group area. The urban edge-pixels were selected as training samples for SVDD based on a profile-based sampling method combining nighttime light value histograms. The results showed that the overall accuracy of SVDD was similar to the support vector machine (SVM) model. However, kappa coefficients of SVDD for highly developed cities were superior to SVM, as producer and user accuracies of SVDD were almost equal to show high agreement of urban and nonurban areas. For metropolitan areas, such as Beijing and Tianjin, the urban extent generated by SVDD is closer to the reference data. The between the quantity of SVDD-estimated urban extent and population, 0.86, was higher than that obtained from SVM, 0.76, indicating that the estimated urban extent from the SVDD is more efficient for understanding the population development. The SVDD was further applied for three other representative metropolitans in China: Shanghai, Guangzhou, and Shenzhen to validate the SVDD’s performance, and similar results were achieved. The success of the SVDD-based urban extent extraction improves our ability to map urban extent at regional and national scales.