This letter presents a novel method for automated footprint extraction of building facades 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 principal component analysis (PCA) method 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 the random sample consensus algorithm. For each building, points in facade planes are chosen to calculate the direction, the start point, and the end point of the facade footprints using PCA. Finally, footprints of different facades of building are refined, harmonized, and joined. Two data sets of downtown areas and one data set of a residential area captured by Optech's LYNX mobile mapping system were tested to verify the validities of the proposed method. Experimental results show that the proposed method provides a promising and valid solution for automatically extracting building facade footprints from mobile LiDAR point clouds.