Cool Mobile / Airborne Project

We have another pretty neat mobile and airborne LiDAR project here in Orlando and the data has just started to materialize.  Over the next couple of weeks, I’ll be posting the data and discussing some of the neat things about it all.  I’ll first start by laying out the project and what we’re trying to do with it and then start discussing the results as we get into the analysis portion of the project.

The project is located in Orlando, FL down near the southern junction of I-4 and SR 417.  We are supporting a resurfacing project that is about 5 miles in length.  The goal of the project is to see if we can save the design engineers time and money by using mobile LiDAR to collect the corridor and give them an Engineering-grade model of the existing paved surface.  They will use this information to design the resurfacing project and hopefully save on materials in the field by using an accurate model of the existing conditions.

Traditionally, this information was collected through the use of Low-Altitude Mapping and Photogrammetry (LAMP) or by surveying cross-sections along the project.  Both of these technologies work and are proven to be accurate, but nothing can beat using a digital terrain model built from millions of points, right?  That is what we’re going to use and we are just getting some preliminary data from our partners in this project, Riegl Corporation.

We have a sneak peek of their new scanners, the VMX-250.  This scanner is pretty amazing and has caused us to re-write our software to handle the large amounts of data it generates.  The graphic below shows an ulfiltered data set of a portion of the project.

417 Project by Drive Line

The colors represent each drive line captured in each direction.  There are a total of 2 drive lines here, each drive line is collecting data from 2 scanners.  As you can see, there is a bunch of “junk” in there, but if you look in 3D mode, you can see that the drive lines are calibrated pretty well.

417 by Drive Line in 3D

All of the data above was collected in 2 passes with 2 scanners which is pretty amazing.  I have all 4 loaded up and the data size is over 10Gb for about a 1-mile portion of the project.  So, as you can tell, there is a ton of data to review, edit and mine for this project!

417 by Intensity and Profile

I’ll end this post with the graphic above showing a cross-section of the road and a view of it by intensity.  We’ll be working with this data over the next few weeks and as we get some results, I’ll post them here!

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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!