Date of Award:

5-2016

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Ziqi Song

Committee

Ziqi Song

Committee

Anthony Chen

Committee

Joseph A. Caliendo

Abstract

Highway inventory plays an important role in highway management. Governments and agencies have been employing different kinds of methodologies for highway inventory. Existing methodologies include field inventory, photo/video log, integrated global positioning system (GPS)/ global information system (GIS) mapping, aerial/satellite photography, terrestrial light detection and ranging (LiDAR), mobile LiDAR, and airborne LiDAR. Each has advantages and disadvantages as well as limitations in collecting road inventory data. This paper mainly focused on the application of airborne data collection method.

Four highway sections in Utah were mapped in this experiment: one section on Interstate 84 (I-84), two sections on Interstate 15 (I-15 north and I-15 south), and one section on US-191. Both LiDAR point cloud data and high-resolution aerial imagery data were obtained. This project mainly focused on processing and analyzing the LiDAR point cloud data by using ArcGIS, but also provided an automatic road sign detection algorithm based on MATLAB for the aerial images.

A comprehensive introduction to highway inventory methodologies, especially airborne LiDAR technology, was provided to relevant departments and personal 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 and to extract desirable features from raw LiDAR point clouds. In addition, a MATLAB-based feature extraction algorithm was also proposed to demonstrate the effectiveness and economic efficiency of the airborne data collection system.

The results showed that although small signs (e.g., speed limit signs) along highways cannot be identified successfully because of the low point density of airborne LiDAR data, other features, such as guardrails, median strips, light poles, and large signs, are very easy to detect. Also, from airborne LiDAR data, one can detect features like culverts and bridges, which cannot be detected by mobile mapping or other inventory techniques. Furthermore, airborne LiDAR data provide accurate coordinate information for the detected highway features. And for aerial images, we can also extract some kind of assets based on the assets' color, shape or other characteristics.

The findings of this research can be used as a reference for the Utah Department of Transportation (UDOT) and other state DOTs before they choose a methodology to collect highway inventory data. Also, the LiDAR-data-based, and image-based road sign extraction methods, may provide inspiration for future researchers to develop more effective and efficient methods for road sign detection.

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