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Jason Amadori's LIDAR GIS BlogArchive for General
Another 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.
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!
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!
