United States Department of Transportation
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Development of an Online Platform for Streamlining Highway LiDAR Data Collection, Sharing, and Processing
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2016-12-01
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[PDF-3.19 MB]
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TRIS Online Accession Number:01569607
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Edition:Final Report April 2015 – August 2016
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Abstract:Lidar (a portmanteau of "light" and "radar.") is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light. Among Tech Report applications, lidar is particularly useful for detailed mapping of highway and bridges, highway surface deterioration monitoring, highway information modeling, highway safety analysis, and rapid post-disaster damage assessment. Current spatial resolution of lidar technology can be as precise as millimeter with static terrestrial laser scanning and centimeter with mobile terrestrial laser scanning. The field is rapidly maturing in capabilities, applications, and utility. With more and larger-scale applications of lidar technology happening each day in the transportation sector, effective management and utilization of the collected lidar data, often very large in size, has become a critical issue. Due to their size, lidar data are often delivered to state transportation agencies on hard drives along with Tech Report deliverables. A transportation agency often need to procure and set up expensive hardware and software to effectively use these data sets, not to mention the amount of training required for its employees. Because of these limitations, lidar data with the exception of specifically derived products such as bridge clearance, which are often acquired for a specific transportation program, are difficult to be effectively used across multiple divisions and programs. There is a great need for a data infrastructure that can minimize state agencies' investment in hardware and software but still would allow state agencies to store, stream, visualize, and analyze lidar data on demand. This study investigated methods and platforms for managing, sharing, visualizing, and processing of massive point cloud data sets. The study starts with synthesizing existing studies on point cloud data visualization. We systematically characterized point cloud data sets in terms of their data volume, variety, variability, and their processing needs in highway applications. A web-based platform was developed in this study to enable visualization of large-scale point cloud data sets in common web browsers. The web portal is tested using several types of lidar data, including data collected along a large segment of Route 1 in New Jersey. The test demonstrated the effectiveness of the developed portal. The outcome of this research provides a versatile tool for state DOTs to leverage various lidar data sets in their asset management programs as well as in future construction projects.
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