Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

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.

Airborne Laser Scanning (ALS) point cloud segmentation and classification are very important and attractive tasks that still interest many scientists. In this paper, 3D classification optimization method is applied for of airborne LIDAR point cloud, covering an urban area with a low density. Our main contribution is to solve classifier and kernel hyperparameters tuning issue. Typically, choice of parameters is done empirically. In almost all classification studies, no details are given for the classifier parameters choice, which is very important. The parameters of the classifier have a direct impact on the classification results. Finding hyper-parameters leads to optimal classification results. In order to determinate the hyper-parameters, authors propose to apply in the context of LiDAR data, a method proposed in the literature called parameters selection to deduce the best parameters, in order to optimize the classifications results. The results shown that parameters selection does not systematically lead to hyper-parameters, but can be used as first stage, then a fine search can be performed around the obtained interval.