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Jason Amadori's LIDAR GIS BlogArchive for Mobile Data Collection
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.
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
