Automated Extraction of Building Facade Footprints from Mobile LiDAR Point Clouds

We present a novel method for automated extraction of building facade footprints from mobile LiDAR point clouds.The proposed method first generates the georeferenced feature image of a mobile LiDAR point cloud and then uses image segmentation to extract contour areas which contain facade points of buildings,points of trees and points of other objects in the georeferenced feature image.After all the points in each contour area are extracted,a classification based on eigenvalue analysis and profile analysis is adopted to identify building objects from point clouds extracted in contour areas.Then all the points in a building object are segmented into different planes using RANSAC algorithm.For each building,points in facade planes are chosen to calculate the direction,the start point,and the end point of the facade footprint using eigenvalue analysis.Finally,footprints of different facades of building are refined,harmonized,and joined.The experimental results show that the proposed method provides a promising and valid solution for automatically extracting building facade footprints from mobile LiDAR point clouds.