Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results' accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach.

Automated summary

This paper presents a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram, dividing the building point cloud into three zones and recognizing high tree crown points by analyzing normal vectors and the curvature factor. The suggested approach was tested on five datasets with different point densities and urban typology, showing high efficacy with accuracy values of 97.9%, 97.6%, and 95.6%.