G-ABORE: regularizing complex geometry building boundaries from pre-processed building LiDAR points using Bézier curve

The increasing use of Light Detection and Ranging (LiDAR) point clouds in vector map production highlights the need for a generalized approach to represent complex (linear and curvilinear) building boundary geometries, considering both technological advancements and architectural diversity. In this paper, the aim is to automatically regularize the complex geometric building boundaries extracted from LiDAR point clouds. In order to achieve this aim, building corner points were first detected with the Automatic Building Outline Regularization (ABORE) method. Then, a distance-based statistical method was implemented to determine the edge types representing the building boundary. If the building edges are linear, ABORE corner points are used. Conversely, if they are curvilinear, the edges are regularized using Bézier curves based on Bernstein polynomials. Thus, by generalizing the proposed ABORE method for regularizing only linear building boundaries, a novel method called Generalized Automatic Building Outline Regularization (G-ABORE) is developed to regularize building boundaries in complex geometry. The proposed method was implemented on the open-source Toronto, São Paulo, Minnesota, and locally produced Ordu LiDAR datasets. Regarding accuracy assessments, the overall performance of the novel method for building boundary regularization is 98.76%, 98.87%, 98.11%, and 98.99% for Completeness (Cp), Correctness (Cr), Quality (Q), and F-score metrics, respectively. In addition, RMSE and PoLiS positional accuracy values were calculated as 0.27 m and 0.24 m, respectively. The results demonstrate that the G-ABORE method effectively regularizes complex building boundaries with high accuracy. As a result, it is proven that the developed method can regularize building layers with an accuracy suitable for representation in vector maps, regardless of the structure of building boundary geometries extracted from point cloud data in fields such as architectural design, urban planning, and more. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.