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Jason Amadori's LIDAR GIS BlogAnother Cool Airport Mapping Project
We just recently completed a cool project for an airport client who was having issues with their concrete surface and “pop-outs” caused by extended freeze/thaw weather events. Pop-outs are caused when the surface of the concrete sheds pieces that are about an inch wide and can be anywhere from <1cm to 3cm deep. The following graphic shows what the pop-outs look like in the field.
The client was looking for a way to quantify the number of pop-outs per slab using an automated process to avoid having to survey every pop-out which would prove to be cost-prohibitive based on the overall size of the project.
Earth Eye deployed 2 teams of data collection vehicles to compare the imagery that could be obtained from our right-of-way cameras as well as from our pavement camera.
The pavement camera has a resolution of 1mm and gives us the ability to resolve the pop-outs from a nadir view, making it easier to automate the extraction of these features from the imagery. Also, the nadir view gives us more spatial accuracy, so the locations of the pop-outs can be accurately mapped and then compared with future imagery to help quantify the amount of new pop-outs that have arisen since the last inventory. Furthermore, the gray-scale image provided by the pavement camera provided more contrast between the concrete surface and the pop-out which is much lighter in color. It was determined that the nadir-view pavement camera provided the best starting point, from which to test the automated pop-out extraction process. The following image illustrates a sample pavement image – note the pop-outs are very visible without having to zoom into the image.
The next image shows the results of our automated classification routine without any manual augmentation of missed pop-outs. We are realizing a consistent yield of greater than 95% of pop-outs identified as compared to control slabs that were collected manually in the field. Being able to efficiently map the pop-outs with a very high-yielding and automated algorithm allows us to efficiently map the pop-outs to support maintenance operations for this airport facility.
All of the pop-outs are geospatially referenced, so we can export all of the pop-outs as polygons with an area measurement associated with them. This area can then be converted to a severity and used to prescribe a specific maintenance activity based on the size and depth of the pop-out. The goal of the project was to create a quantified measurement (count) of the pop-outs for this entire project and we successfully completed this task with high-yielding, geospatial results.
Automated Pavement Distress Analysis – The Final Frontier?
We have been working with some automated methods for quantifying crack measurements and have had some interesting results. How great would it be to collect pavement images, batch them on a server and have it spit out accurate crack maps that you can overlay in a GIS? The technology is here! Or, is it?
Most pavement inspections involve intricate processes where pavement experts rate segments visually, either from field visits or rating pavement images in the office. This introduces a lot of subjectivity in the rating results and typically culminates in a spreadsheet showing pavement ratings by segment. The data is then modeled using ASTM performance curves that have been built from industry proven pavement experiments.
There is no doubt that these curves are tried and true representations of how pavement performs in varying physical and environmental conditions and each project should take these factors into consideration when developing the preservation plans for an agency.
We have been working to develop a rating workflow that focuses on a combination of automated and manual processes to bridge the current gap of Quantitative and Qualitative pavement inspections. The way we are doing this is through the application of GIS to the automated rating process. Here’s how it works…
First, we begin with a pavement image from our LRIS pavement imaging system. Images are captured at a 1mm-pixel resolution and then analyzed through an automated image processing workflow.
The resulting image creates a “crack map” that identifies the type, severity and extent of the distresses on that section of pavement. The process is fully automated and handled by the computer.
Once we have the crack maps in place, we then apply a manual editing process that is GIS-centric by nature and the resulting crack map is a more accurate representation of the real-world conditions.
Once the edited crack maps are compiled, the data is exported to a GIS where the extents are calculated geospatially and then integrated with a pavement management system. This is where all of the Pavement Condition Indices (PCI) are calculated and applied to each agency’s specific pavement rating methodologies. Since the process is geospatial in nature, it is easily imported to ANY pavement management software and gives our clients the flexibility to apply any rating methodology they desire.
Of course, all agencies have a certain spending threshold and there are cases where automation is the only way to cost-effectively manage large volumes of data. We recognize this fact and are working hard to bridge the gap of available funding and high quality data.
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.
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.
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:
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Maximum Height of Encroachment Unit
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Average Height of Encroachment Unit
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Total Area (acres) of Encroachment Unit
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Total Area (acres) of Encroachment Units along a particular circuit
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.
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.
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.
The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).
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.
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.
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.
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.
One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!
Mobile LiDAR and Cross-Slope Analysis
DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days. The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR. This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).
This project shows the value that this type of project can provide to the end user on both sides of the fence.
First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract. Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification. This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.
On the other side of the fence resides the FDOT. They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information. If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information. This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.
Here are some screenshots of the information.
LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile
Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.
These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.
Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.
Chasing Cracks…
Not the same crack that is in the news, but it is pretty addicting…
We’ve built a bunch of new tools centered on pavement crack assessment and we’re excited about how it will increase the transparency related to pavement assessments. In the past, pavement assessments have been more about delivering segments with PCI values attached to them and less about the actual measurements that were used during the creation of this data.
Our clients are always quick to say “We went out and checked a few segments and our assessments were different than what was reported”. This lead to an educational discussion about how the ratings were created and how we applied the ASTM methodology to arrive at these results. Most of the time we all agreed that there was always some subjectivity in the ratings, but that the standard rating methodology had been applied the same way throughout the network.
Our goal has always been to increase the transparency related to pavement inspections and this new approach has helped us to take a step in that direction. The process is GIS-centric, as it is with all of our processes and involved a ton of tool development that will continue to evolve over time. So, here’s what we’re doing…
First, we are collecting crack images using a downward-facing 4k linescan camera system with laser illumination. This ensures that all of the pavement images are uniform and are not subject to low-lighting or shadows from natural and man-made features. These images are 1mm resolution, allowing us to see the detailed cracking – especially at the lowest severity levels.
The following graphic illustrates the output from the crack mapping software we are using. Cracks are identified in the imagery automatically from the software and are exported as geospatial points, lines and polygons.
The software does a great job of identifying longitudinal, transverse, and alligator cracking. Once we have the initial crack map, our team of compilers goes in and edits the crack maps as needed. Typically, we are editing out false-positives and adding in other distresses as dictated by the scope of work. This editing is done within our EarthView software and is completely geospatial in nature. In other words, we can export these cracks, so they can be viewed in a GIS. This is pretty exciting because all of these cracks can be mapped and themed in a GIS based on their severity levels.
This process gives the end user of the data a simple QA/QC process that can be used to understand the specific issues related to each segment. Furthermore, this data is then combined with other GIS data sets (Functional Classification, Traffic Counts, etc.) so that a more holistic approach can be taken towards the determination of which segments need in terms of repair methods. This data can also be exported to Google Earth for easy viewing and display in a non-GIS software.
We hope that this provides the end user with more tools in their GIS arsenal to better plan, bid, and execute their Capital Improvement Planning for the year. In other words, our clients will be able to do more with their limited funding than ever before!
Sign Retroreflectivity Compliance – A Geospatial Approach
We just completed a sign retroreflectivity shortlist presentation for the a client and discussed the options available for gaining compliance based on FHWA regulations as described in the MUTCD. The client was sold on the “Blanket Replacement” method by a vendor who specializes in sign replacement.
MUTCD Retroreflectivity Guidelines
I was thinking “what a great selling strategy”, but then I thought twice about it. This vendor had the ability to write their own ticket for selling their sign materials! A great strategy for the vendor, but not a good option for the client.
We approached the presentation using a different approach – it combined the concept of risk with the general principles of Asset Management. First, we would inventory their existing sign network to determine what they had and where it was. Then, we would prioritize which areas were the most likely to fail based on the average age of the signs as well as the risk associated with the actual failure (e.g. pedestrian injury or vehicle damage due to an accident).
Risk Assessment for Signs
Sample Replacement Cost Calculation
This approach takes into consideration the entire segment of a road instead of considering an individual asset. The client believes that it is more cost effective to replace the worst signs along a segment using a single mobilization of field crews, rather than jumping around and fixing signs based solely on their condition. Therefore, we are combining the geospatial location, condition, age, value and MUTCD to develop a risk score for each individual sign.
Project Life Cycle
This analysis is used to create the biggest bang for the buck for our client by reducing risk related to accidents caused by failing signs. Since all agencies have to be compliant with Regulatory, Guide and Warning signs by 2015, this approach will support a phased approach while taking care of the highest risk signs and working through the lower risk signs until all non-compliant signs have been replaced or are scheduled for replacement.
Compliance Dates for Sign Retroreflectivity
Valuation of Sign Asset
In conclusion, the use of Risk to support the prioritization of asset maintenance serves an appropriate role in saving clients time and money. By replacing the highest risk assets first, an agency can reduce their exposure to lawsuits related to failing infrastructure.
Executive Dashboard
URISA Caribbean Conference in Trinidad
I am here for 3 days supporting the URISA Caribbean conference and presented to a group of Utilities who were interested in Asset Management of their electrical infrastructure. It was interesting to hear that they have the same issues that we encounter in the US related to infrastructure preservation funding – Limited budgets, underfunded programs and failing infrastructure.
We always do our best to stay “realistic” when it comes to Asset Management. It has not been a priority in the past and will not be one in the future – until things start failing. The funding will never be there in the amount that it is needed, so what do you do then?
Start Small…
We had a long discussion in this workshop about starting small and working your way into a larger AMS implementation. By starting small, you can increase the probability of success of your project and “correct the path of the ship” if something was missed or overlooked. Select one Asset Type and focus on building it into a comprehensive database of information. By selecting one Asset Type, you can limit the amount of effort and risk it will take to build it into your AMS.
Show it Off…
Once you have some good asset data, show it off to everyone and make sure the decision-makers understand that it is available for use. Build charts, graphs and reports about the Asset and build a plan around getting that Asset into the work cycle for maintenance and operations. One of the most important things I recommend is to identify the “Worst” Assets and get them fixed immediately. These typically create liability for your agency and if left unattended, could cost more than the AMS itself.
Make it Indispensable
Once the AMS is used to make decisions and answer questions related to infrastructure, it will become the “Go To” system that your agency will use moving forward. At that time, you will be able to show the value of the system and gain leverage in terms of future funding, new assets, etc. At this point, no one would make the call to shut this system down because of the value it adds to everyday decision-making.
Those are some recommendations that we discussed this week – looking forward to 2 more days of collaboration and learning about Trinidad’s Utility industry!
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)
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
Raw Point Cloud
- Create Attachment Points at Conductor Location
Select 3 Locations along Line
Software Models Line Geometry in 3D from Point Cloud
Resulting Data set in EarthView Software:
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)
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
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
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.
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
