Abstract: Airborne LiDAR can timely and accurately obtain a large number of 3D point cloud data with accurate 3D position information. It has a wide range of applications in digital cities, forest fire prevention, intelligent transportation, etc. Among them, the 3D point cloud data of urban areas, especially urban central areas with dense plants, is often occluded by tall trees or vegetation which make it difficult to recognize data belongs to man-made objects such as buildings. This paper uses a direct quadratic polynomial fitting method to extract regional information of typical local areas of vegetation and buildings, such as tall trees, and constructs sensitive structural features of regional targets. Furthermore, through fuzzy logic, the task 3D point cloud data classification especially designed to distinguish building targets and disturbances from trees can be completed. The experimental results show that this method can quickly and effectively realize the classification of LiDAR point cloud data, and the proposed method has the good application prospect and robustness for promotion.

Key words: urban buildings, LiDAR, regional feature, quadratic polynomial, data classification