Mobile LiDAR to Support Positive Train Control

This article was originally written in 2011, but is being re-posted based on recent events…

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data.  They are currently collecting airborne data for approximately 15,000 linear miles of rail.  In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction.  The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company.  Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage.  All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies.  We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

IMAG0166IMAG0168

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

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Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data.  Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail.  The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

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So, back to the discussion about Airborne PTC vs Mobile PTC data.  Here is a signal tower collected by Airborne LiDAR.  The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

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The next graphic shows the detail of the same Asset feature from the mobile LiDAR data.  It is much easier to identify the Asset feature and Type from the point cloud.  In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery.  By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

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This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

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One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

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Who is Checking Your LiDAR Data?

Throughout the years, I have seen many projects advertised, awarded, executed and then delivered to the client. The client receives the data, copies it locally and then final payment is made to the vendor and life goes on as usual. Then, someone actually checks the data and notices that there are many discrepancies associated with the scope of work and what was actually delivered. How does this happen and how can it be avoided?

Step 1 – Start with a Clear Scope of Work

The scope should define exactly what is going to be collected, how it will be collected and how it will be verified and checked after delivery. For example, a simple LiDAR scope must define the target point densities (LiDAR), hydro-flattening parameters, and accuracies (absolute and relative) for the project. The scope should also define how the client will be checking the data for final acceptance of the deliverables.

Step 2 – Process a Pilot Area

The pilot area should be representative of the overall project and should be processed and delivered as if it was its own project. This allows for the team to identify any processing issues or special techniques up-front so that the rest of the project can move forward in a linear fashion, thus limiting the re-visiting of the data to fix problems at a later date. Once the pilot area is delivered, it should be checked against the scope of work to ensure that all deliverables are being met in accordance with the client’s expectations.

Step 3 – Process the Entire Project

Final processing can occur once the pilot area is collected and accepted. This is a critical-path item that is the bulk of the project’s budget. Many projects will either be successful or a turn into a disaster during this phase. The risk is easily mitigated, though, as long as the first two steps of this process are in place and properly executed by the team. This is very reliant on communication between the vendor and the client and if these channels are in place, the project will most likely run smoothly since everyone is on the same page.

Step 4 – Data Validation and QA/QC

This is where the overall success of a project is either validated or issues are identified that must be resolved before final delivery is accepted. The processes for checking these data sets are specific for different type of deliverables – we will focus on some niche market deliverables and give examples of how to check their associated data elements.

LiDAR QA/QC

First off – make sure you have some kind of software that can open this data. Seems simple, but many clients do not have the most rudimentary piece of the puzzle – LiDAR viewing software. There are many commercial-off-the-shelf (COTS) products that can be used and each one has its strengths and weaknesses. The goal is to be able to load the entire project in one place and then use the tools within the software to verify the deliverables. The most important items to check include:

· Average Point Density across the project

· Relative (flight line to flight line) accuracies – this should be half of the stated RMSE for the project (e.g. 5cm for a 9.25cm RMSEz spec or 7.5cm for a 15cm RMSEz spec.)

· Absolute (overall project) accuracies against ground control. Ground control should be on a hard surface and un-obscured and is typically tested to a 95% absolute accuracy specification). A minimum of 20 points is required, since one point out of 20 will get you to the 95% specification. Larger areas can require significantly more control.

· Data classifications (e.g. Ground, Vegetation, Overlap Points, Low Point/Noise, etc.) as per the project specifications (ASPRS or USGS publishes these specifications).

· Check terrain edits (look for berms that are removed, building points in ground, low point noise and other anomalous data in the wrong classes).

· Projection information in the LAS file header.

· Verify Intensity TIFFs as per user-specified requirements.

· If breaklines are required, check the following:

          o Water bodies meet minimum size criteria,

          o Interior points classified to water class and

          o Client-specified buffers around these features

          o Single drains (streams) meet minimum length and width requirements and are buffered as per client specifications.

          o Double-line drains (Rivers) are monotonic (perpendicular elevations to remove leaning) and are buffered as per client specifications.

          o For all breaklines – check elevations are at or slightly below terrain for a sampling of tiles for the project (typically 10% of project).

· Review the survey report

· Flightline trajectories with appropriate metadata, flight logs, and other raw data collection activities (GPS, inertial, etc).

· Metadata for all project deliverables (this can be automated with a metadata parser).

In conclusion, it is important to check your data immediately upon receipt, so that all quality control and quality assurance activities can be performed and verified while the data is still relevant. Good luck!

Transmission and Distribution Utility Infrastructure Capital Planning; A LiDAR and GIS-Centric, Data Fusion and Risk-Based Prioritization Approach

Introduction

Now that the NERC alert bubble has burst, the transmission and distribution sectors of the power industry has a wealth of information that can be leveraged to enhance their business operations. Most power companies are using LiDAR, Imagery and GPS data to collect detailed information about their infrastructure and this information can be leveraged to develop a GIS-centric Asset Management database. So, what can an agency do to leverage this information, especially when it comes from multiple vendors, sensors and vintages?

First, it is important to find the common denominator between all of the data the agency is working with. Utility data typically uses a Structure ID or Span ID that can be used to tie all of this information together from a database perspective. The location of the Structure or Span can also be used to tie information together geographically from a mapping perspective as well as temporally for those agencies collecting information annually or as part of a particular inspection time series.

Next, the agency can visualize all of this information spatially utilizing a GIS so that spatial patterns can be observed. Typical spreadsheet-based deliverables are missing the spatial relationships that can be used to develop better maintenance and operation plans by observing how assets interact with one another. This spatial perspective adds another valuable dimension to help agencies prioritize where to spend their limited resources.

Finally, a Risk-Based prioritization model can then be developed to help the agency decide where to spend their limited funding resources. The assets that pose the highest risk score based on the Probabilities of Failure and the Consequences of those failures can be prioritized, thus limiting the risk to the agency based on these types of failures.

LiDAR Data Collection, Utility Asset Extraction, and Inspection Data Aggregation

LiDAR data can be captured from fixed-wing aircraft or helicopter platforms, depending on the required resolution of the data. Most agencies are interested in capturing information about features that are located within the right-of-way of a powerline or its associated structures. These features are classified in the point cloud and then modeled using encroachment measurement criteria to identify potential hazards to the powerline infrastructure.

The LiDAR point cloud can be used to model the existing as-built structures, tops of towers, conductors, as well as the bare-earth ground model of the area. This information is then loaded into PLS-CADD software and modeled at a maximum load (sag) and maximum blowout conditions. Any LiDAR features that intersect with these “safe zone envelopes” are flagged as encroachments and will be highlighted in the PLS-CADD reports. These reports are exhaustive in terms of the amount of good information contained within them, but can be overwhelming to an agency when trying to figure out “where” to start focusing their time and resources on corrective actions.

1_PowerlineDefinitionfromPointCloud

Once all of this analysis has been performed, these encroachment features can be geospatially located and mapped for further analysis. For example, vegetation encroachments can be identified as either “grow-in” or “fall-in” potentials and these points are classified as such.

Vegetation Encroachment Management

GIS mapping provides the user the spatial context necessary to make informed Operations and Maintenance decisions.  As an example, the location of vegetation encroachments is known and with a little manipulation, the volume and area of the vegetation can be determined very easily.  This gives an agency the ability to control the costs associated with their vegetation management program.  Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.

 2_2DVeg_EncroachGrowinFallin

The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process.  In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information.  A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed.  Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is.

Risk-Based Asset Prioritization of Work Activities

Once your agency has identified where the encroachment issues are, how do you design a plan of action that gives your agency the biggest bang for your buck? In other words, there may be a section of powerline that contains many different encroachment types – Vegetation, Building, Ground Clearance, etc. Another section of line may only have Vegetation encroachments. The agency is most likely handling the corrective actions for these issues out of multiple departments and for good reason. Each type of encroachment brings its own set of design standards or engineering challenges to the table and all of these needs to be considered when designing a corrective action program for the facility.

One criterion that can be applied to this information is the concept of Risk.  Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types.  The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus.  In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets.  By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.

3_Risk_Condition

Risk models can be very complicated or very simple. It is dictated based on the information you wish to maintain moving forward and can use multiple automated inputs to help ease the data management strain moving forward. For example, an agency is using their LiDAR information to calculate the risk to a facility based on the number of LiDAR points that have been identified as encroachments as well as their height above ground; the higher the point, to more risky it is to the facility. In other words, the higher the vegetation feature, the more risk it poses to the facility. Since LiDAR data is composed of 3D points, the densities of these points can be applied to the facility’s risk score and then used to help prioritize the facilities that need the most work immediately.

Developing a Project Matrix and Estimating Costs Using Budget Forecasting

Once the facilities have been prioritized using the Risk concepts described above, the agency can then start planning for the actual work activities that will need to happen as part of their annual capital improvement planning activities. This can be achieved by using the Risk scores to determine which facility needs to be worked on and how much it will cost to improve that facility.

First, the facility components can be modeled from the LiDAR point cloud. As a simple example, we can imagine a distribution facility composed of a wood pole, conductors, cross-arm, guy wires and associated hardware. Each one of these facility components has a cost component associated with it based on the materials used and the characteristics of how it was constructed. The cost of materials can then be applied to each component and an overall facility cost can then be determined for the asset.

4_VegetationStatistics

Once the facility templates are constructed, the agency can then start developing projects to improve or replace these facilities based on the results of the inspection information. This activity will allow the agency to determine the cost of a project in relation to their annual maintenance and operations budgets and then determine what they can improve for that fiscal years’ time frame.

Project_Matrix

All of this information can then be used to determine future years’ capital improvement plans based on funding availability and projected costs over time. This helps the agency to plan for future fiscal expenditures using a repeatable and defensible model that can be applied to different Asset Classes and Asset Types. In other words, multiple, disparate data sources can be fused to support the risk-based prioritization of work activities.

What are you going to do with your NERC data?

So, you’ve collected your entire Transmission network using LiDAR, built your PLS-CADD models and identified your encroachments – what’s next?  How about leveraging that data to manage the Work Activities required to upgrade/maintain your Transmission network?

We have all heard about Asset Management and how it can help an agency extend the useful life of its infrastructure.  We all know that in principal it makes all the sense in the world, but the actual application of these concepts require investment in software, hardware and personnel.  What we will never know is – How much should we invest in the management of our assets?  Using the NERC regulation and the frenzied data collection going on in our industry as an example, consider the following.

Most Airborne LiDAR companies are collecting and delivering data in the $500 – $1,500 per linear mile range, depending on the downstream processing requirements.  Most of this data is delivered to the end user as .LAS point clouds, PLS-CADD .BAK, files and some other CAD or GIS formats.  Once it is delivered, the agency has a unique opportunity to leverage the delivered products for future value.

If we use Vegetation Encroachment data as an example, we can illustrate how the encroachment information can be used to create a vegetation Asset Class and managed throughout its life-cycle.  Most likely, the data delivered to an agency will include .LAS point clouds with classified data reflecting terrain, conductors, towers, buildings, etc.  In addition to this, vector data is also delivered and can be used to support maintenance management activities.  The graphic below illustrates a common Transmission LiDAR deliverable.

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Note the Red vegetation in the graphic above.  It shows the vegetation points that have been flagged as encroachment violations based on its proximity to the conductors.  These points can then be mapped in a GIS or Asset Management program for further analysis.  In doing so, an agency can gather more value from this information.  For example, the graphic below illustrates the “grow-in” (light blue) and “fall-in” (red) violations for a section of Transmission line.

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GIS mapping provides the user the spatial context necessary to make informed vegetation management decisions.  First, the location of vegetation encroachments are known and with a little manipulation, the volume and area of the vegetation can be determined very easily.  This gives an agency the ability to control the costs associated with their vegetation management program.  Asset management software that leverages GIS can provide the tools necessary to develop an immediate return-on-investment of the software purchase and associated data collection expenditures.

First, the user creates the geospatial layers from the classified point cloud.  Vegetation violations can be exported as points and then aggregated into vegetation encroachment units.  These units are then integrated with the Work and Asset management system through the use of GIS.  Since the geometry of the encroachment units are known based on its GIS attributes, an agency can then determine the following characteristics about their encroachments:

  1. Maximum Height of Encroachment Unit
  2. Average Height of Encroachment Unit
  3. Total Area (acres) of Encroachment Unit
  4. Total Area (acres) of Encroachment Units along a particular circuit

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Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.  The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process.  In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information.  The graphic below shows a KMZ export of Vegetation Encroachments that can be provided to field units in charge of vegetation removal.

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A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed.  Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is.  Pretty amazing concept to embrace because now an agency can accurately predict the costs of their vegetation management program.

Another factor that can be applied to this information is the concept of Risk.  Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types.  The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus.  In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets.  By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.

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None of this stops once you get to the Work Management piece of the puzzle.  I’ll be providing more information related to tracking the work activities as they are completed in the field and using this information to develop more accurate budget forecasts for the future.

Mobile LiDAR to Support Positive Train Control

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data.  They are currently collecting airborne data for approximately 15,000 linear miles of rail.  In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction.  The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company.  Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage.  All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies.  We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

IMAG0166IMAG0168

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

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Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data.  Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail.  The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

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So, back to the discussion about Airborne PTC vs Mobile PTC data.  Here is a signal tower collected by Airborne LiDAR.  The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

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The next graphic shows the detail of the same Asset feature from the mobile LiDAR data.  It is much easier to identify the Asset feature and Type from the point cloud.  In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery.  By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

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This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

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One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

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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…

Airborne / Bathymetric Fusion

We just completed a project for a private landfill here in FL to help settle a contractor dispute about how much dirt was moved/removed from a retention pond.  The problem stemmed from the fact that the design engineer estimated the volume as one amount of cubic yards and the earthworks guys sent a bill for twice that amount!

Project Site

We thought it would be easy by collecting it with airborne LiDAR as part of our flight testing, but then realized that the area in question was a pond that was under water!  So, back to the drawing board…

Back in my RCID/Disney days, I worked with some smart people and we learned how to integrate GPS and Bathymetric sensors to map the Hydrilla in their lakes.  We also gathered some useful Bathymetric data that could be used to determine target concentrations of herbicides based on a specific dilution factor.  The most important part of that equation was knowing the amount of water in the lake and it was a math formula from there on forward.  Divide the volume by the target concentration level and you had the amount of herbicide needed to make the brew.

GPS Track of Bathymetric Data

So, we went old school and used our RTK rover to supply a GPS location and the Bathymetric sensor to grab the Z (depth) values for the lake in question.  The collection took about an hour and we had a processed and calibrated bathymetric surface before leaving the project site.  From there, we integrated the bathy data with the airborne LiDAR to get a continuous representation of the underwater surface.

Solid Rendering of Bathymetric Data with Airborne LiDAR

There was a small discrepancy between the water elevation on the day of airborne collection and the bathy collection.  This was handled by surveying the water elevation on the day of the bathy collection and then adjusting all of the depths to this elevation (corrected for the transducer offset which was about 0.1 foot).  This gave us the correct elevations relative to the airborne LiDAR data set.

Profile of Bathymetric Data Showing an "Empty" Lake

We determined that the volume of dirt removed was the same as the yield as determined by the design engineer.  It turns out that the contractor might have to come to the table to prove that they moved more material then the design engineer predicted and we confirmed with this cool project!