Classifying urban landscape in aerial LiDAR using 3D shape analysis

The classification of urban landscape in aerial lidar point clouds is useful in 3D modeling and object recognition applications in urban environments. In this paper, we introduce a multi-category classification system for identifying water, ground, roof, and trees in airborne lidar. The system is organized as a cascade of binary classifiers, each of which performs unsupervised region growing followed by supervised, segment-wise classification. Categories with the most discriminating features, such as water and ground, are identified first and are used as context for identifying more complex categories, such as trees. We use 3D shape analysis and region growing to identify ¿planar¿ and ¿scatter¿ regions that likely correspond to ground/roof and trees respectively. We demonstrate results on two urban datasets, the larger of which contains 200 million lidar returns over 7km². We show that our ground, roof, and tree classifiers, when trained on one dataset, perform well on the other dataset.