One of the pet-peeves I have with asset management software is that they are basically Access on steroids. There’s not much to the inner workings of the software other than showing you information about an asset – its location, street name, type and maybe some historical information. Once you get past the attributes, the applications get very complicated because they need to handle some business logic and one-to-many relationships, etc. The list of attributes can be exhaustive, but how much of it is useful when making a decision about what to do to an asset with limited funding.
I’m working with a vendor who understands this relationship and the role it plays in prioritizing asset rehabilitation and we are getting some promising results. Imagine trying to prioritize which roads you will be resurfacing next year based on their condition alone. The worse condition they are in, the higher priority they will receive when you are ranking them based on condition alone.
What if you added risk analysis into the mix?
Then, you can ask these questions of your data – If this road fails, what kind of impact will it have on the travelling public? Does the road carry large volumes of traffic, or is it in the boonies?
If you can answer these questions, you add another level of intelligence to your data. Start incorporating traffic volumes, functional classification, detour and access constraints into your model and you’re on your way to prioritizing with intelligence.
Here’s some LiDAR data from the City of Charlotte Pilot project. The images below shows the different data stored in the point cloud. This information is useful to our team of processors during the data extraction phase. For example, the image below shows the LiDAR point cloud themed by intensity. The intensity information can be used to extract different types of vector data such as the road centerline, edge stripes, pavement markings and other special pavement markings. The intensity values can be used to assess the condition of the markings based on its reflectivity. Basically, the markings are reflective or they’re not and that is a representation of their condition.
Pavement Markings from LiDAR Point Cloud Intensity
The point cloud can also be used to generate contours. The image below shows contours that are 0.1-foot. A trained eye can see the usefulness of this data in that a roadway transition is occurring in this area.
Elevation Data Displaying 0.1-foot Contours
The next image shows a roadway profile from the same area. This profile information can be used to generate cross-sections at any interval along the roadway.
We are operational!
Aircraft – Check!
LiDAR – Check!
Aerial Photography Camera – Check!
Hyperspectral Camera – Check!
LiDAR Data Colored by Elevation
We’re operational and have a ton of data in the can and ready for processing. Our data sets include samples from residential communities to transmission powerlines to unmentionable clients who have some interesting needs! One of the biggest hurdles has been developing our own viewing software that we can deliver with these large datasets so that our clients can manage their deliverables. The goal is to build a piece of software that is lightweight and easy to maintain code-wise, while building tools that clients can use to streamline their business processes.
3"-pixel Aerial Photography
Keep watching here for data samples and updates to our software!
We’re finishing up on the City of Charlotte’s pilot pavement data collection project. To date, we have collected Mobile LiDAR, Mobile Video, Ground-Penetrating Radar, Roughness and Rutting data for a 50-mile pilot area. This was the first go-around for our pavement camera and the results were great.
What’s cool about this is that we can now get a great view of the lane of travel and see the low density cracking because we’re basically collecting 2mm pixels. We’re working on learning how to orthorectify these images so they can be fused with the point cloud to give a real-world representation of the pavement surface that can be viewed in 3d.