Road extraction from Light Detection and Ranging (LiDAR) has become a hot topic over recent years. Nevertheless, it is still challenging to perform this task in a fully automatic way. Experiments are often carried out over small datasets with a focus on urban areas and it is unclear how these methods perform in less urbanized sites. Furthermore, some methods require the manual input of critical parameters, such as an intensity threshold. Aiming to address these issues, this paper proposes a method for the automatic extraction of road points suitable for different landscapes. Road points are identified using pipeline filtering based on a set of constraints defined on the intensity, curvature, local density, and area. We focus especially on the intensity constraint, as it is the key factor to distinguish between road and ground points. The optimal intensity threshold is established automatically by an improved version of the skewness balancing algorithm. Evaluation was conducted on ten study sites with different degrees of urbanization. Road points were successfully extracted in all of them with an overall completeness of 93%, a correctness of 83%, and a quality of 78%. These results are competitive with the state-of-the-art.
Keywords: airborne LiDAR point clouds, road point extraction, bidirectional skewness balancing