The authors present a method for extraction of newly-built buildings in road-region using morphological attribute profiles and one-class random forest. The morphological attribute profiles are first obtained from bitemporal high-resolution remote sensing images. The morphological attribute profiles obtained and spectral features are then combined to extract newly-built buildings along road-regions using an improved one-class random forest. Bitemporal images of the Daoxiang Lake area in Beijing are used as experimental data to validate the proposed method, by quantitatively comparing with two conventional change detection methods, i.e., direct bitemporal classification and post-classification comparison methods based on support vector machine. The experimental results show that the accuracy of newly-built building extraction from the proposed method (i.e. using combined spectral features and attribute profiles) is significantly higher than that using only the spectral features, with an increase of 15.11% in Kappa. In addition, the Kappa of the proposed method is 1.78% and 25.15% higher than that of the direct bitemporal classification and that of the post-classification comparison. Therefore, the experimental results validate the effectiveness of the proposed method. Advantages of the one-class random forest include capabilities to effectively deal with high-dimensional data and measure the importance of different features used in one-class classification.