Building segmentation and classification from aerial LiDAR via local planar features

Abstract

In this paper, we propose a framework on building segmentation and classification from Aerial Lidar data via planar features. In this framework, the planar points corresponding to planar objects are obtained first by an unsupervised Markov random field clustering model. The ground normal is detected from planar points via the proposed constrained K-means algorithm. Within constrained K-means algorithm, the building points are generated by removing ground points from planar points. Furthermore, the candidate buildings are obtained by using region growing algorithm. Finally, these candidate buildings are classified into two types, that is, abnormal building and normal building based on the proposed vertical feature. Experimental results on a real world dataset demonstrate the effectiveness of our framework.