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Field Verified Floor Plans

How are you launching your urban infill projects? If you are not using field verified data to build your existing conditions models you are baking error into your project.

RSA has multiple options to produce everything from 2D floor plans to full Revit Building Information Models – and all from field verified data sets. Check out this short video for examples that may prove right for your next project!

Construction Verification for Architectural Cladding

RSA’s ReCap Group was in Atlanta this week collecting data to assist metal fabrication installers. Architectural cladding with metal continues to be a popular choice for commercial architects due to the wide variety of finishes and applications. However, this variety also means that the panels are typically custom made to fit. Variances between the design drawings and the as-built conditions can result in deviations that are beyond an installer’s capabilities to repair on-site. This means stopping the install while new measurements are taken, a replacement panel is cut, and then shipped back to the site from the factory.

In order to avoid these delays our client has RSA capture the as-built conditions so that they can compare them to the design drawings before any panels are cut. Changes in panel and support pieces are made to account for the existing substructure conditions, everything is cut, stacked on the truck in reverse order of installation and shipped for assembly onsite. Schedules are maintained, product is not wasted, and everyone is happy.  Not bad for a morning’s work in Atlanta!

Deciding to Buy or Lease 3D Tech? Ask Yourself these Questions

As some of you may have heard, I am no longer with SmartGeoMetrics (SGM) and have taken a position as Director of Reality Capture at Ragan-Smith Associates. It’s been an interesting shift. Ragan-Smith has been one of my clients for more than 8 years and I suddenly find myself less a hired gun than an inside man. You would think that during all of that time as a 3D modeler, field technician, hardware provider, and consultant for them that I would have a pretty good idea about how things work there (here!).

The truth is, while nothing I thought was wrong, there were a lot of details that I missed by being on the outside.

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Field Test Part Two: Comparing UAV Volumetric Data to Lidar

Let’s start with the product pile that was on site. As you may recall, I captured it with both a Leica C10 and with a DJI Phantom 3 Professional. At first, both data sets looked great. In order to eliminate as many variables as possible, I exported a point cloud from Pix4D and brought it into Leica Geosystems Cyclone for comparison with the C10 data. Since you can choose the density of point cloud when creating it in Pix4D, density issues would seem to be a moot point. However, I am of the opinion that point sampling from a mesh is like choosing the number of digits after zero when measuring: You should not sample at an interval that is greater than your per-pixel resolution as it gives the impression of higher accuracy than actually exists.

That being said, our Ground Sampling Distance was 1.2 in/pixel (3.05 cm/pixel) so we set our point cloud sampling spacing to match. The first thing that jumps out is how much denser the C10 data is. However, this has a much smaller effect on the overall accuracy then our next discovery.

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Field Test: How Accurate is UAV Survey?

The buzz (sorry couldn’t resist) around UAVs is undeniable, but if I learned anything from laser scanning it’s that you shouldn’t use the manufacturer’s tech sheet to quote your capabilities.

With UAVs, this is even more of an issue as there are so many more variables to contend with. While I often think of the UAV as little more than a flying tripod, the fact is that flight control and geo-referencing options can greatly affect the outcome of projects. Then, you have issues of what type of camera to use, and that’s all before you even consider the software you intend to use for processing. However, none of this seems to deter the plethora of UAV-based service providers that call and email me each and every week looking for work.

What it comes down to is this: I don’t believe most of their claims. I simply do not understand how they can achieve the accuracies that they claim to attain. So, I decided to start running a few tests to see what was not just possible, but predictably achievable in a project setting, outside of a lab.

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