Abstract
Abstract. 3D road mapping is essential for intelligent transportation system in smart cities. Road features can be utilized for road maintenance, autonomous driving vehicles, and providing regulations to drivers. Currently, 3D road environment receives its data from Mobile Laser Scanning (MLS) systems. MLS systems are capable of rapidly acquiring dense and accurate 3D point clouds, which allow for effective surveying of long road corridors. They produce huge amount of point clouds, which requires automatic features classification algorithms with acceptable processing time. Road features have variant geometric regular or irregular shapes. Therefore, most researches focus on classification of one road feature such as road surface, curbs, building facades, etc. Machine learning (ML) algorithms are widely used for predicting the future or classifying information to help policymakers in making necessary decisions. This prediction comes from a pre-trained model on a given data consisting of inputs and their corresponding outputs of the same characteristics. This research uses ML algorithms for mobile LiDAR data classification. First, cylindrical neighbourhood selection method was used to define point’s surroundings. Second, geometric point features including geometric, moment and height features were derived. Finally, three ML algorithms, Random Forest (RF), Gaussian Naïve Bayes (GNB), and Quadratic Discriminant Analysis (QDA) were applied. The ML algorithms were used to classify a part of Paris-Lille-3D benchmark of about 1.5 km long road in Lille with more than 98 million points into nine classes. The results demonstrated an overall accuracy of 92.39%, 78.5%, and 78.1% for RF, GNB, and QDA, respectively.
Highlights
Light Detection and Ranging (LiDAR) is becoming a leading technology in data acquisition, LiDAR is significant with its rapid, direct and accurate technique in capturing different objects with point density that covering each object with a sufficient number of points
Dataset slicing was implemented using equal number of points (250,000 pts./slice) in addition to two sides overlap with 50,000 points each and a cylindrical neighbourhood was carried out through whole points to find their neighbours within a 0.20 m radius
The use of machine learning in 3D mobile LiDAR point cloud classification is still interesting to many researchers
Summary
Light Detection and Ranging (LiDAR) is becoming a leading technology in data acquisition, LiDAR is significant with its rapid, direct and accurate technique in capturing different objects with point density that covering each object with a sufficient number of points. ML is the ability of the classifier to learn the experience from a pre-classified dataset automatically, and improves the classification ability of unknown dataset. It is the process of solving any problem through getting known dataset, learning from it and building a model that is able to expect unknown dataset (Burkov, 2019). Automatic labelling any point cloud through ML classifiers is based on three main stages; neighbourhood selection, features extraction and the classification stage. We evaluate three ML classifiers for mobile LiDAR point clouds data classification. The extracted features are used an input to ML classifiers and accuracy assessment of the three classifiers is presented
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