Modeling residential urban areas from dense aerial LiDAR point clouds

Abstract

We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we propose a robust classification algorithm that effectively classifies LiDAR points into trees, buildings, and ground. The classification algorithm adopts an energy minimization scheme based on the 2.5D characteristic of building structures: buildings are composed of opaque skyward roof surfaces and vertical walls, making the interior of building structures invisible to laser scans; in contrast, trees do not possess such characteristic and thus point samples can exist underneath tree crowns. Once the point cloud is successfully classified, our system reconstructs buildings and trees respectively, resulting in a hybrid model representing the 3D urban reality of residential areas. © 2012 Springer-Verlag.