Mobile LiDAR to Support Roadway Resurfacing

We just completed a mobile LiDAR project that was designed to support a roadway resurfacing project in Orlando.  The project was centered on the use of mobile LiDAR to generate roadway profile data that Engineers could use to design a resurfacing project.  Obviously the data would need to be accurate and we were able to hit the mark and best of all – prove it!

Overview of SR417 Project

We collected the data using the new Riegl VMX-250 mobile LiDAR unit using a single pass in the north and southbound directions.  We only required one pass in each direction to collect the road data which makes it very efficient from the data collection standpoint.  In the past, we had to collect “strips” of data and then “sew” them all together during the calibration process.  In this case, we took the opposing (NB and SB) strips and calibrated them relative to one another and then they are brought down to control as a final step.

LiDAR Coverage by Flight Line

Most of our clients are interested in the overall accuracies of the data, so we have built accuracy assessment tools that make it easy to review the LiDAR against survey control.  The tool is simple to use and allows us to sort the results and dig deeper into the least accurate points to see why there might be discrepancies in the control vs the TIN surface.

417 Accuracy Control Report

For this project, we achieved an RMSE of .0525 ft – calculated by comparing the control elevations (Z) against the TIN elevations (Z TIN).  This is important because we can check the point cloud against known control that was collected throughout the project and provide detailed information about the accuracy of the data.

Once the data has been calibrated sufficiently, we can then generate all of the derivative products for this project.  We generated the following data for our roadway engineers:

  • Pavement Cross-Slope
  • Shoulder Cross-Slope
  • 3D Roadway Markings
  • Edge of Friction Course

3D Vector Data

This data set also supports detailed engineering analysis related to guardrail height above the roadway.  This is an important factor to consider because there are specific standards that define where the guardrail is placed, more specifically, its height above the roadway, to corral vehicles that end up impacting the guardrail in an accident situation.  The following graphic displays how this measurement can be made in the point cloud data.

Guardrail Height Measurements

Advertisements

Tunneling through the Trees

Just finished collecting a site for a project with some massive overhead trees.  This wreaks havoc on the GPS signal and validates the importance of having a good Inertial Measuring Unit (IMU) on board to carry the trajectory of the vehicle during the GPS outage.  This section of road is a virtual “tunnel” of trees which makes it difficult to nail the accuracy specification of 0.1-foot with GPS alone!



Elevation Data of Roadway Obscured by Trees

The next graphic shows the same area displayed by Intensity:

Intensity Image of Canopy Road

Here is a 3D Version of the Elevation point cloud:

3D Elevation Point Cloud

And here is an elevation profile of the same area:

Elevation Profile of 3D Point Cloud

Over the next couple of weeks, we will be creating 3D vectors from these point clouds that will be used to determine the geometric characteristics of the roads.  This information will then be used as parts of safety audits related to the following information:

  • Radius of Curvature
  • Horizontal Curve (Cross-Slope)
  • Vertical Curve (Grade)

This data will then be used with crash data to determine if a road needs to be re-aligned based on its geometric characteristics.  For example, a tight curve with a high speed may be contributing to crashes on a segment of road.  This information can be used to understand precisely how a road is constructed and functioning in the real world.

Airborne Accuracy Assessment

We’re moving along with one of our mobile/airborne projects and we just received an independent control assessment and the results are looking great!  We have achieved a project-wide RMSE of about .1112 feet for the airborne portion of this project – which is pretty good for airborne…

SR417 Project Extents

The way we check this is by loading the ground control and LiDAR data into our data viewer and then running a control report against the data.  Basically, we’re intersecting the ground control with the TIN model of the ground class of points.  The Z values are checked against one another and the difference is calculated.  These results are then used to create an RMSE for the project based on the control results.

This is a good way to get an idea of how well the data has been calibrated in terms of absolute accuracy.  Once we get a control report, we typically sort by the worst result and then start examining the control and the surrounding terrain.  The graphic below shows how we can sort the results and then “Go To” the control point in question.

Zoom to Control Point to Examine Local Conditions

We can see how the point is being assessed against the terrain.  Sometimes, there is a blunder in the terrain model and we might be able to edit the terrain to make sure it is the true ground surface.  Elevated objects such as trees can influence the accuracy assessment, but sometimes, it might be as subtle as a gutter drain, as seen in this next graphic.  The profile view of the drain cross-section shows how the terrain is influencing the accuracy assessment.

Control Point Cross-Section Showing Uneven Terrain

The goal in the future will be to collect all control on uniform areas that are not subject to sudden terrain changes.  This will ensure that the TIN correctly models the surface that is being checked for accuracy.  The next graphic shows the actual results for this project…

Control Report for Airborne Data

Our next step will be to check the control against the mobile data.  We should have that in about a week or so…

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!

Cool Mobile / Airborne Project

We have another pretty neat mobile and airborne LiDAR project here in Orlando and the data has just started to materialize.  Over the next couple of weeks, I’ll be posting the data and discussing some of the neat things about it all.  I’ll first start by laying out the project and what we’re trying to do with it and then start discussing the results as we get into the analysis portion of the project.

The project is located in Orlando, FL down near the southern junction of I-4 and SR 417.  We are supporting a resurfacing project that is about 5 miles in length.  The goal of the project is to see if we can save the design engineers time and money by using mobile LiDAR to collect the corridor and give them an Engineering-grade model of the existing paved surface.  They will use this information to design the resurfacing project and hopefully save on materials in the field by using an accurate model of the existing conditions.

Traditionally, this information was collected through the use of Low-Altitude Mapping and Photogrammetry (LAMP) or by surveying cross-sections along the project.  Both of these technologies work and are proven to be accurate, but nothing can beat using a digital terrain model built from millions of points, right?  That is what we’re going to use and we are just getting some preliminary data from our partners in this project, Riegl Corporation.

We have a sneak peek of their new scanners, the VMX-250.  This scanner is pretty amazing and has caused us to re-write our software to handle the large amounts of data it generates.  The graphic below shows an ulfiltered data set of a portion of the project.

417 Project by Drive Line

The colors represent each drive line captured in each direction.  There are a total of 2 drive lines here, each drive line is collecting data from 2 scanners.  As you can see, there is a bunch of “junk” in there, but if you look in 3D mode, you can see that the drive lines are calibrated pretty well.

417 by Drive Line in 3D

All of the data above was collected in 2 passes with 2 scanners which is pretty amazing.  I have all 4 loaded up and the data size is over 10Gb for about a 1-mile portion of the project.  So, as you can tell, there is a ton of data to review, edit and mine for this project!

417 by Intensity and Profile

I’ll end this post with the graphic above showing a cross-section of the road and a view of it by intensity.  We’ll be working with this data over the next few weeks and as we get some results, I’ll post them here!

Mobile LiDAR Update

Here’s some LiDAR data from the City of Charlotte Pilot project.  The images below shows the different data stored in the point cloud.  This information is useful to our team of processors during the data extraction phase.  For example, the image below shows the LiDAR point cloud themed by intensity.  The intensity information can be used to extract different types of vector data such as the road centerline, edge stripes, pavement markings and other special pavement markings.  The intensity values can be used to assess the condition of the markings based on its reflectivity.  Basically, the markings are reflective or they’re not and that is a representation of their condition.

Pavement Marking Condition Assessment

Pavement Markings from LiDAR Point Cloud Intensity

The point cloud can also be used to generate contours.  The image below shows contours that are 0.1-foot.  A trained eye can see the usefulness of this data in that a roadway transition is occurring in this area.

Elevation Data Displaying 0.1-foot Contours

The next image shows a roadway profile from the same area.  This profile information can be used to generate cross-sections at any interval along the roadway.

Elevation Profile

Airborne LiDAR Update

We are operational!

Aircraft – Check!

LiDAR – Check!

Aerial Photography Camera – Check!

Hyperspectral Camera – Check!

Avalon Park LiDAR

LiDAR Data Colored by Elevation

We’re operational and have a ton of data in the can and ready for processing.  Our data sets include samples from residential communities to transmission powerlines to unmentionable clients who have some interesting needs!  One of the biggest hurdles has been developing our own viewing software that we can deliver with these large datasets so that our clients can manage their deliverables.  The goal is to build a piece of software that is lightweight and easy to maintain code-wise, while building tools that clients can use to streamline their business processes.

3"-pixel Aerial Photography

Keep watching here for data samples and updates to our software!