AI Summary & Key Takeaways
AI InsightThe quality control basically performed qualitatively by visual inspection and quantitatively by calculation of overall accuracy, which is conduct by comparing the proposed algorithm results with data provider’s ground classification and Cloth Simulation Filtering (CSF) algorithm’s results.
📌 3 Key Takeaways
Abstract
Terrain models play a key role in many applications, such as hydrological modeling, volume calculation, wire and pipeline route planning as well as many engineering applications. While terrain models can be generated from traditional data sources, an advanced and recently popular geospatial technology, Light Detection and Ranging (LiDAR) data, is also a source for generating high-density terrain models in the last decades. The main advantage of LiDAR technology over traditional data sources is that it generates 3D point clouds directly so that the representation of the surfaces is obtained fast. On the other hand, before terrain modeling, ground points need to be extracted by point labeling in the 3D point cloud. In this study, a new algorithm is proposed for automatic ground point extraction from airborne LiDAR data for urban areas. The proposed algorithm is mainly based on height information of the points in the dataset and labels ground points comparing height differences in local windows. The algorithm does not require any user input threshold and a neighborhood definition. The proposed ground extraction algorithm was tested with three different urban area LiDAR data. The quality control basically performed qualitatively by visual inspection and quantitatively by calculation of overall accuracy, which is conduct by comparing the proposed algorithm results with data provider’s ground classification and Cloth Simulation Filtering (CSF) algorithm’s results. The overall accuracy of the proposed algorithm is found between 95%-98%. The experimental results showed that the algorithm promises reliable results to extract ground points from airborne LiDAR data for urban areas.