Multifeatures Graph-Cut Algorithm for Extraction of Buildings From Large-Scale Urban LiDAR Data

Building extraction from airborne LiDAR data is significant for the accurate modeling of urban structures and constitutes a pivotal step in urban 3-D reconstruction. However, in complex scenarios with noise, uneven point density, and the coexistence of buildings with sparse vegetation or shrubs, existing geometric features (e.g., curvature) struggle to distinguish planar from nonplanar regions, leading to extraction errors. To overcome this limitation, we propose the adaptive tensor-weighted planar descriptor (ATPD), a novel planarity feature that employs second-order tensors and reconstructs the covariance matrix through a data-driven adaptive weighting scheme. This design significantly improves the separability of planar and nonplanar regions under challenging conditions. Comparative experiments further confirm that ATPD consistently outperforms traditional geometric features such as roughness, omnivariance, planarity, and curvature. In addition, planar features alone are insufficient to capture regions with local variations (e.g., roof ridges). To address this, we integrate planar and mesh-based features and develop a multifeature graph-cut algorithm for robust building extraction from large-scale point clouds. The proposed method is validated on three benchmark LiDAR datasets of Vaihingen provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), where it achieves the best performance with a quality of 90.3% and an $F1$ -score of 94.9%, outperforming traditional methods. Compared with deep learning approaches, our method also yields higher correctness (Corr). Further experiments on large-scale datasets from Vaihingen, Germany, and Palmerston North, New Zealand, confirm its robustness, successfully extracting all buildings larger than 50 m2.