Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach

Abstract

Building detection plays an important role in urban applications and is usually a prerequisite for contour extraction and building modeling. Over the last decades, airborne LiDAR data have been used due to its capability to represent terrestrial surfaces and objects with high geometric quality. In this paper, it is proposed a novel building detection approach based on geometric/morphological object characteristics. The proposed strategy is divided into three main stages: 1) selection of candidate points based on height; 2) building detection using the geometric feature (omnivariance) and K-means clustering algorithm; and 3) refinement based on majority filter and mathematical morphology. The experiments were conducted using airborne LiDAR datasets with varying point density acquired in different urban environments. The results indicated the robustness of the proposed approach for all datasets and environmental complexities, presenting average Fscore of around 96%. In addition, the results pointed out that point density can impact the building detection, producing better results for higher point density datasets. Compared with related approaches, the proposed strategy results in better performance in terms of completeness, producing an omission error rate smaller than 3%.