Building Roof Superstructures Classification from Imbalanced and Low Density Airborne LiDAR Point Cloud

Abstract

Light Detection and Ranging (LiDAR), an active remote sensing technology, is becoming an essential tool for geoinformation extraction and urban planning. Airborne Laser Scanning (ALS) point clouds segmentation and accurate classification are challenging and crucial to produce different geo-information products like three-dimensional (3D) city designs. This paper introduces an effective data-driven approach to build roof superstructures classification for airborne LiDAR point clouds with very low density and imbalanced classes, covering an urban area. Notably, it focuses on building roof superstructures (especially dormers and chimneys) and mitigating nonplanar objects’ problems. Also, the imbalanced class problem of LiDAR data, to the best of our knowledge, is not yet addressed in the literature; it is considered in this study. The major advantage of the proposed approach is using only raw data without assumptions on the distribution underlying data. The main methodological novelties of this work are summarized in the following key elements. (i) At first, an adapted connected component analysis for 3D points cloud is proposed. (ii) Twelve geometry-based features are extracted for each component. (iii) A Support Vector Machine (SVM)-driven procedure is applied to classify the 3D components. (iv) Furthermore, a new component size-based sampling (CSBS) method is proposed to treat the imbalanced data problem and has been compared with several existing resampling strategies. In this study, components are classified into five classes: shed and gable dormers, chimneys, ground, and others. The results of this investigation show the satisfying classification performance of the proposed approach. Results also showed that the proposed approach outperformed machine learning methods, including SVM, Random Forest, Decision Tree, and Adaboost.