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Jason Amadori's LIDAR GIS BlogArchive for lidar
LiDAR Editing Freak!
So, I have been working with the guys to keep my feet wet with LiDAR data editing so that I understand more about what it takes to prepare the LiDAR surface for delivery to the client or for an ortho surface. I got my share of data and headed off to edit my surface…
Sounds pretty easy, right? Well, it actually is as long as you know what to look for. Our data for this project had a lot of low points in it – due mostly to the fact that we are shooting down stormwater grates in neighborhoods. This creates low points in the data that is not indicative of the true terrain. We usually filter these out using our filtering algorithms, but sometimes these points still exist in the data and need to be edited out.
The first way to identify a low point is to create a TIN of the surface. If there are low points, the TIN will be dragged down by the surface and there will be a gaping hole in the surface. Another way to identify these holes is to look at the color palette of the scene and if it does not have the usual distribution of colors – Red to Purple – there is a low point somewhere in the scene.
Low Point in TIN Surface
We can also see the low point using the “Profile” view – it can be seen below the surface.
Low Points Below the Filtered Surface
These points can be re-classified and removed from the Ground Classification and placed into the “Low Point / Noise” Classification and then the surface is modified. Note the better distribution of the color palette for the scene…
Resulting TIN Surface
Finally, the resulting profile shows the points reclassified to the correct classification. Repeat for each tile until complete!
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!
More Mobile Data
We’ve wrung out the calibration issues with our mobile LiDAR and we have a solid solution that we can hang our hat on. We have achieved RTK-equivalent accuracies for short runs of less than 5 miles and today, we’re heading out to see how well the accuracies hold up for a 30-mile run.
No worries about having this ready for prime time today – most projects we’re dealing with are only 3-5 miles right now. Our goal is to figure out the best way to keep the GPS solution in check for long runs to avoid post-processing and calibration issues on the back-end of the collect. Since most of the required measurements are “relative”, the LiDAR data is good, but we are striving to crack the “absolute” accuracy nut – and that involves a solid calibration of the equipment. We’ve always known this, but actually “doing” that is a different story!
This is a 5-mile run that we collected here locally. The data has been filtered to a bare pavement surface and we have run cross-sections every 5 feet. Each cross-section can be exported to CAD, GIS, Microstation, etc and used to build pavement resurfacing design drawings.
We have been able to make the processing of this data “semi-automated”. Basically, we have to draw in the breaklines which typically correspond to the pavement stripes. These define the lanes of travel and then we do a slope calculation (relative measurement) from one breakline to another – effectively calculating the cross-slope percentage for each lane. We’re also exporting out a tabular format so clients can use that information to verify the values against a pavement design spec.
Slope 1 refers to the left lane (southbound) and Slope 2 refers to the right lane of travel (northbound) and all slopes are percentages.
Heading to the SPAR Conference
We’re heading to Houston, TX to the SPAR conference next week to demo some new mobile and airborne data and processing tools. This is a great conference for the mobile mapping community and most of our competitors will be there with their latest technology.
We created a marketing video via YouTube to show off the tools we have and how they work – check it out:
Mobile LiDAR Update
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.
Elevation Profile
Airborne LiDAR Update
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!
Pavement Camera Update
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.
Pavement Cam
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.
