Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data.The machine learning-based methods for segmenting point clouds suffer from the imprecise computation of the training feature values. The most important factor that influences how precisely the feature values are computed is the neighborhood chosen by each point.
This research addresses this issue and proposes a suitable adaptive neighborhood selection approach for individual points by completely considering the complex and heterogeneous nature of the input LiDAR point cloud data. The proposed approach is evaluated on high-density mobile and low-density aerial LiDAR point cloud datasets using the Random Forest machine learning classifier.
In the context of performance evaluation, the proposed approach confirms the competitive performance over the state-of-the-art approaches. The computed accuracy and F1-score high-density Toronto and low-density Vaihingen datasets are greater than 91% and 82%, respectively.
Three-dimensional (3D) Light Detection and Ranging (LiDAR) point cloud data segmentation is a prominent area of research in remote sensing, photogrammetry, and computer vision. Due to the rapid development of technology, it is now possible to obtain LiDAR point cloud data using mobile laser scanning (MLS), terrestrial laser scanning (TLS), and aerial laser scanning (ALS) [1]. Those technologies can extract LiDAR point cloud data from a complex urban environment and the acquired data have been used in various 3D urban scene analysis applications, including building extraction [2–4], road identification [5], power line identification [6], vegetation cover analysis [7], and urban scene segmentation [8, 9]. The individual point in aerial LiDAR point cloud data contains Cartesian coordinates (X, Y, Z), where X, Y, and Z represent each point’s latitude, longitude, and height, respectively. In addition to these coordinates, it may also contain additional properties, such as RGB color information. The segmentation of individual points is quite challenging due to outliers, partial loss, and uneven density in the captured LiDAR point cloud data [10].
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