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 geoinformation 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.

Tilt detection method for transmission line tower construction based on quadratic point cloud matching

“…The improved iterative nearest method is used for fine matching, which not only avoids the dependence of the algorithm on the initial position of the point cloud [4] , but also enhances the matching accuracy of the missing point cloud. The specific process is as follows: First, the normal vector of each point in the transmission line tower point cloud needs to be estimated.…”

Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping

“…A study on the application of SMOTE for balancing data distribution with land cover mapping using LiDAR data showed increased detection accuracy. The challenges associated with imbalanced classes and low density of LiDAR point clouds in urban areas were also satisfactorily resolved by applying several oversampling methods for the classification and extraction of roof superstructures [12]. Due to its proven advantages in classification, the present study used SMOTE, a method for producing synthetic new data from existing ones, which provided new information and variations to synthetically generated data.…”