Some Cool Powerline Data to Support NERC’s Rule 810 Recommendation

With the recent release of NERC’s “Recommendation to Industry” on October 7th, 2010, we have been contacted by many transmission companies to LiDAR their transmission lines.  The recommendation asks all Transmission Planning Engineers, Transmission Maintenance Engineers and Transmission Planners to measure and report any “Facility Ratings” that do not meet their intended design criteria based on actual field conditions.  The plan to achieve this must be submitted to NERC by December 15th, 2010.

This post discusses how we are supporting the industry with this compliance effort utilizing LiDAR, imagery and spatial analysis.

The first step in the process is the data collection effort focused on collecting precise powerline data and calibrating it to local control to achieve engineering-grade accuracies.

Classified LiDAR Point Cloud (Brown = Ground, Green = Vegetation)Classified LiDAR Point Cloud (Brown = Ground, Green = Vegetation)

The next process involves the creation of 3D vectors from the point cloud.  First, the raw point cloud is loaded into our Viewer.

Powerline Data Displayed by Elevation

Powerline Data Displayed by Elevation

 

 

 

 

 

 

 

 

Raw Point Cloud

Raw Point Cloud

 

 

 

 

 

 

 

Create Attachment Points at Conductor Location

Create Attachment Points at Conductor Location

 

 

Select 3 Locations along Line

Select 3 Locations along Line

Software Models Line Geometry in 3D from Point Cloud

Software Models Line Geometry in 3D from Point Cloud

 

 

 

 

 

 

 

Resulting Data set in EarthView Software:

Transmission Lines Modeled in 3D

Transmission Lines Modeled in 3D

 

 

 

 

 

 

 

 

Once the lines are modeled, we work with the agency to define the search criteria for performing the spatial analysis and encroachment analysis in the software.  We load these parameters and automatically identify the encroachments related to vegetation features and conductors.

Vegetation Encroachments Modeled in 3D and Highlighted in Blue (Grow-in) and Orange (Fall-in)

Vegetation Encroachments Modeled in 3D and Highlighted in Blue (Grow-in) and Orange (Fall-in)

Orthographic View of Potential Violations

Orthographic View of Potential Violations

 

 

 

 

 

 

 

 

Parcel data is then used to deploy field crews and empowers them with local information in the case they need to determine who is responsible for vegetation encroachment and property access.

Owner Information Incorporated into Vegetation Analysis

Owner Information Incorporated into Vegetation Analysis

 

 

 

 

 

 

 

 

Planimetric data is also created so that the information can be used to update line drawings and as-built information.

Planimetric Data Incorporated into Point Cloud

Planimetric Data Incorporated into Point Cloud

Planimetric Data Incorporated into Point Cloud

Planimetric Data Incorporated into Point Cloud

 

 

 

 

 

 

 

 

At the end of the day, we are able to generate many different types of deliverables for our clients.  The keys to this process include accurate calibration of data, 3D vector generation, encroachment automation, and industry-specific delivery formats.

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Getting Ready for Some Rail Data

We are going to have some rail data collected here soon and I’ll post the results as soon as I get them.  We’ll be collecting a 17-mile section of track in the DC area, so it will be full of urban canyons, tunnels and highway overpasses.  At least we’ll know how well the equipment will be performing in the field because this will be a true field test of the equipment.

We just finished getting e-Rail certified to work with the railroad, so now it is time to provide some value to this market.  They are looking to collect features that will support Positive Train Control as well as giving them engineering-grade information that can be used for maintenance & rehabilitation efforts.

Rail Intensity

Our goal will be to collect all of this information in one pass and give them tools to automate the extraction of other features such as the rail, ballast, gauge, etc.  Data collection will happen next week and we’ll have some results to share at that point.

Rail LiDAR with Elevation

Rail Data Themed by Elevation

Rail Data with Intensity

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

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…

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!

LiDAR Editing Freak!

So, I have been working with the guys to keep my feet wet with LiDAR data editing so that I understand more about what it takes to prepare the LiDAR surface for delivery to the client or for an ortho surface.  I got my share of data and headed off to edit my surface…

Sounds pretty easy, right?  Well, it actually is as long as you know what to look for.  Our data for this project had a lot of low points in it – due mostly to the fact that we are shooting down stormwater grates in neighborhoods.  This creates low points in the data that is not indicative of the true terrain.  We usually filter these out using our filtering algorithms, but sometimes these points still exist in the data and need to be edited out.

The first way to identify a low point is to create a TIN of the surface.  If there are low points, the TIN will be dragged down by the surface and there will be a gaping hole in the surface.  Another way to identify these holes is to look at the color palette of the scene and if it does not have the usual distribution of colors – Red to Purple – there is a low point somewhere in the scene.

Low Point in TIN Surface

We can also see the low point using the “Profile” view – it can be seen below the surface.

Low Points Below the Filtered Surface

These points can be re-classified and removed from the Ground Classification and placed into the “Low Point / Noise” Classification and then the surface is modified.  Note the better distribution of the color palette for the scene…

Resulting TIN Surface

Finally, the resulting profile shows the points reclassified to the correct classification.  Repeat for each tile until complete!