A robust and efficient building segmentation from the LiDAR point clouds

3D modelling is an important task in urban planning and notably smart cities. For this purpose, many remote sensing technologies have been proposed for acquiring 3D data such as Mobile Laser Scanning (MLS) and Aerial Laser Scanning (ALS) platforms. These two systems provide densely point clouds including not only the positioning data but also giving information about 3D attributes such as elevation. This study uses both ALS and MLS point clouds in order to produce 3D model of urban buildings. The proposed algorithm consists of three main steps; (i) pre-processing, (ii) building detection, and (iii) 3D modelling of the extracted buildings. Regarding the pre-processing, a considerable amount of points related to the ground and noisy points are eliminated due to accelerating in the time of computation. In the next step, 2D location of each building, is extracted using the interactive extraction method. Also, the model and type of roofs are acquired from this step too. Finally, a Random Sample Consensus (RANSAC)- based approach separates the walls of each building which would lead to the modelling of them by fitting a 3D cube. The algorithm was evaluated in an urban region with area of about 6 hectares. It played an acceptable role in the both detecting and modelling of buildings with the 98% (precision and recall) and 0.05 (RMSE).