Classifying built-up areas from satellite images is a challenging task due to spatial and spectral heterogeneity of the classes. In this study, a contextual classification method based on conditional random fields (CRFs) has been used. Spatial and spectral information from blocks of pixels were employed to identify built-up areas. The CRF association potential was based on support vector machines (SVMs), whereas the CRF interaction potential included a data-dependent term using the inverse of the transformed Euclidean distance. In this way, accuracy was stable for a varying smoothness parameter, while preserving class boundaries and aggregating similar labels, and a discontinuity adaptive model was obtained and conditioned on data evidence. The classification was applied on satellite towns around the city of Nairobi, Kenya. The accuracy exceeded that of Markov random fields, SVM, and maximum likelihood classification by 1.13%, 2.22%, and 8.23%, respectively. The CRF method had the lowest fraction of false positives. The study concluded that CRFs can be used to better detect built-up areas. In this way, it provides accurate timely spatial information to urban planners and other professionals.