Random forests ensemble classifier showed to be suitable for classifying mutlisource data such as lidar and RGB image for urban scene mapping. However, two major problems remain : (1) the class boundaries are not well classified, a common issue in classification (2) the data are highly imbalanced raising another issue more specific to urban scenes. In this paper, we propose a new ensemble method based on the margin paradigm to improve the classification accuracy of minor classes. Random forests classifier is used in a two-pass methodology with an improved capability for classifying imbalanced data.