A Building Point Set Extraction Algorithm in Vegetated Scenes

Abstract

Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point set extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point set in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. In addition, we perform mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of façade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. On the Urban-LiDAR dataset, the method achieved a precision of 98.74%, a recall of 98.47%, and an F1 score of 98.60% for roof extraction. For facade extraction on the same dataset, the precision, recall, and F1 score were 97.98%, 70.94%, and 82.30%, respectively. On the Vaihingen dataset, the proposed method outperformed the PointNet network by 20.73% in roof extraction precision and achieved comparable performance with the state-of-the-art HDL-JME-GGO network. For facade extraction, the method surpassed the PointNet network by 49.63% in precision, the PointNet++ network by 16.53%, and fell slightly behind the HDL-JME-GGO network by only 3.87%. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees.

Keywords: vegetated scenes, building point set, geometric information, mask polygons.