Utilizing Mobile LiDAR to Support Pavement Resurfacing

Many Departments of Transportation are looking for ways to save money while increasing safety on the roads. In order to do this, they are seeking out innovative ways to do this while utilizing new technology. Mobile LiDAR is being used to determine roadway geometry information for long stretches of roadways that are candidates for resurfacing. The typical DOT procurement process involves the selection of a resurfacing vendor through a competitive bid solicitation and then the selection of the most qualified and “cost-effective” bidder. As budgets have become leaner, the competition for these projects has increased and thus, drives the innovation curve to find the most cost-effective solution for the DOT.

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To achieve this goal, pavement vendors have sometimes turned to the use of LiDAR information to develop their bid packages for the DOT. Historically, vendors would use the as-built information that was available from the DOT which might be inaccurate, old or obsolete. This obviously leads to issues with the information that the pavement vendor uses to develop their bid packages. They are most interested in determining the correct amount of cut/fill needed to resurface the road while using the least amount of new material. One of the most important pieces of this puzzle relates to the cross-slop of the road which facilitates roadway drainage and ultimately makes a road safer for the traveling public.

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Mobile LiDAR provides a high-precision, digital terrain model of the roadway surface that can be used to generate very accurate cross-slope measurements at specific intervals. For example, the road surface is continuous for the entire length of the project. Cross-Slopes can be generated for each travel lane as well as for the shoulders. The extracted cross-slope is then compared to the design specification and colored based on whether it is in compliance or out of compliance.

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Once the areas have been identified that are out of compliance, it is easy for the pavement vendor to target those for the re-design effort. Instead of applying an average value across the entire section of road, specific areas can be identified and re-designed so that the pavement vendor can save the DOT money on materials. The ultimate benefit for both the pavement vendor and the DOT lies in the fact that everyone benefits – Pavement vendors can design roads more accurately and limit their risk of material over-runs while the DOT can select the most cost-effective vendor and have more budget available to pave their ever-increasing network mileage of roads.

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Since mobile LiDAR data is very cumbersome to manage (2Gb/mile) it is important to deliver the data in a format that is usable by the client. Sometimes raw LAS files work and sometimes the client can only deal with vector files that will be used in GIS, Autocad or Microstation, to name a few. We have found that KMZ files are useful as a delivery mechanism because they can be easily loaded and viewed by the client in very short order. Any derivative of these delivery mechanisms will work – it just depends on the expertise of the client and their computing environment.

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Future discussions will focus on the DOTs and their collection of mobile LiDAR data so that they can provide it to all of the pavement vendors and receive the most cost-effective bid packages. Although there is an up-front cost associated with the LiDAR collection, it is believed that the downstream cost savings for both the DOT and the pavement vendor will more than outweigh the up-front cost of collecting the mobile LiDAR data.

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Mobile / Bathymetric Data Fusion

When we were at the ILMF conference, I had someone stop by and ask us about fusing Mobile LiDAR and Bathymetric data.  So, I asked him for an XYZ file and we made the import into EarthView.  The data looked great calibration-wise and the data sets seemed to line up pretty well from a high level.  His main concern was related to data editing portion of the project.

As with any LiDAR project – it is pretty easy to collect and calibrate the data, but making it useful for analysis is the hard part!  The Bathy portion of this project had a lot of noise in the point cloud and required some re-classification for sure as shown in the graphic below.  The Blue data is the Mobile data and the Green data is the Bathy data – each is colored to its corresponding point cloud.

Mobile and Bathymetric Data by Line

The Floating blue points are mostly the water surface, but some of it is floating above the surface and needs to edited out of the point cloud.  We handle this by using our editing tools to re-class those points into the “Water” class so that it has a home in the point cloud.  The next graphic shows how we can re-class those points with editing and the resulting “Hole” in the point cloud where the Water class has been turned off.

Mobile/Bathy Water Edits

Please note that all of the data hasn’t been edited for this demo, just a subset to show the editing tools.

Here is a profile view of some boats parked in their slips – this shows above-ground features and underwater features simultaneously.

Boat Slips Above and Below Water Line

Once again, this is all “cool” in terms of pictures, but there is a lot of noise in the data that needs to be hand-edited before a true surface can be created with the data.  We’re working this data as we speak and I’ll post more about it when we’re finished editing!