Automatic building outline extraction from ALS point cloud data using generative adversarial network

Abstract

AbstractThe generation of building footprints from laser scanning point clouds or remote sensing images involves three steps: segmentation, outline extraction and boundary regularization/generalization. Currently, existing approaches mainly focus on the first and third steps, while only few studies have been conducted for the second step. However, the extraction result of the building outlines directly determines the regularization performance. Therefore, high-quality building outlines are important to be delivered for the regularization. Determining parameters, such as point distance and neighborhood radius, is the primary challenge in the process of extracting building outlines. In this study, a parameter-free method is proposed by using an improved generative adversarial network (GAN). It extracts building outlines from gridded binary images with default resolution and no other input of parameters. Hence, the parameter selection problem is overcome. The experimental results on segmented point cloud datasets of building roofs reveal that our method achieves the mean intersection over union of 93.52%, the Hausdorff distance of 0.640 m and the PoLiS of 0.165 m. The comparison with α-shape method shows that our method can improve the extraction performance of concave shapes and provide a more regularized outline result. The method reduces the difficulty and complexity of the next regularization task, and contributes to the accuracy of point cloud-based building footprint generation.

Keywords

• extraction of building outlines
• point cloud data
• GAN
• deep learning