Implementation of aerial LiDAR technology to update highway feature inventory.
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2016-12-01
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Abstract:Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of
transportation networks. They guide, warn and protect drivers, and regulate traffic. To manage and maintain the
regular operation of the highway system, state departments of transportation (DOTs) need reliable and up-to-date
information about the location and condition of highway features. Different methodologies have been employed to
collect road inventory data. Currently, ground-based technologies are widely used to help DOTs continually update
their road database, while air-based methods are not commonly used. The focus of this project is to analyze the
capability and strengths of an airborne data collection system in highway inventory data collection.
In this study, a field experiment was conducted to collect both light detection and ranging (LiDAR) point cloud
data and high-resolution aerial imagery data. A comprehensive introduction to highway inventory methodologies,
especially airborne LiDAR technology, was provided to relevant departments and personnel to promote their
understanding of the pros and cons of different inventory techniques. An ArcGIS-based algorithm was developed to
analyze and process LiDAR data as well as extract desirable features from raw LiDAR point clouds. In addition, a
MATLAB-based drainage grate detection algorithm was proposed to demonstrate the effectiveness and economic
efficiency of the airborne data collection system. The detection results were provided and compared with a Mandli
dataset. Economic comparison between airborne LiDAR and mobile LiDAR was also provided. From the results of
this project, we can conclude that airborne LiDAR technology is a promising method for road inventory data
collection.
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