Automatic Ground Extraction for Urban Areas From Airborne Lidar Data

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.

Applications of LiDAR in Agriculture and Future Research Directions

“…Ground truth data were subsequently obtained from aerial photographs of a complex urban area to classify buildings, trees, asphalt roads and the ground with respective accuracies of 77.90%, 58.37%, 72.90% and 71.53%. A ground point extraction automated algorithm based on the height difference of ground points and non-ground points for each point on three LiDAR data sets was proposed and evaluated quantitatively and qualitatively by Sevgen et al [ 57 ]. The overall accuracies were calculated to be 95%, 97%, and 98% for the three LiDAR data sets.…”

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

“…Active and passive remote sensors have been successfully used for vegetation monitoring, for purposes such as species classification, health status, and tree allometry assessment. Light detection and ranging (LiDAR) techniques have been demonstrated to be an efficient and accurate method for distinguishing between trees and vegetation in urban environments [ 6 , 7 ]. Spectral imaging and LiDAR data have been combined to identity woody crop species [ 8 ] in different kinds of orchards, including sweet chestnut.…”