 Yeah, so I'm actually going to use the example of road infrastructure serving from UAS LiDAR I'm from Tel-Dyne Geospatial Most people haven't heard of that name. Formally we refer to as Tel-Dyne Optec. Optec's been manufacturing LiDAR systems for over 40 years. We've manufactured airborne systems, mobile scanning systems, terrestrial LiDAR systems, and now UAS systems. So road infrastructure projects, they vary quite significantly in the range of requirements based on really the nature of the work required. So starting off in a greenfield application, you can basically, it's primarily looking at topographic surveys which are relatively low accuracy since you're looking at just generating contour plots and an overview map of the area. Once you actually go into a construction phase, this is where the accuracy requirements tend to increase quite significantly. So as those projects start, you get into earthworks and aggregate work. Moving forward you get into some paving work and at the end of towards the end of the project you're doing quality control and often for quite large projects, take for example, if you're doing a highway that's tens of kilometers long, these these different stages of the construction project will overlap and as a result if you look at the deliverables required, they're going to overlap quite significantly as well. Thank you. So for example, when you're starting with the earthworks, that's going to be primarily a lower accuracy topographic survey and as you get towards the end when you're doing a comparison to the as-built plan, you really need engineering grade data which in the past has been traditionally collected through manual survey methods, terrestrial laser scanning, or mobile mapping methods. And then finally, once you've actually got a completed road and you're doing a basically maintenance on that road, those deliverables can again be such things as the as-built drying and the engineering grade and then as well as mapping other assets close to the road. So for example, electric utilities and vegetation inventory. The other thing that is becoming more important is basically the change monitoring. What's happening to the road and the area around it because as you have for example over different seasons and frost freeze cycles and the heaving as a result, that road's going to change over time. So one of the methods or one of the things I like to use as a reference because it's really one of the worst-case scenarios that has the highest accuracy requirement is looking at the curb required or modeling the curb. So here's an example from mobile mapping data that gives you an idea of the type of accuracy that customers expect or are looking for from the data. And so from this you can see you can pick out the edge of the pavement, you can pick up the gutter, the face of the curb as well as the back of the curb surface and then to the left of that is the grass. Typically from UAS slider though, this is much harder to pick up. So this is an example from a typical UAS slider that most people are familiar with and you can see like as humans there's a curb there, but you really can't pick out the exact features. So for example that face of the curb, if you look at the width of those points there, that's roughly 20 centimeters. So it's really hard to pick up those accurate as-built information from that. And so what are the reasons for that? Well, it's a combination of beam divergence, the ranging precision of the systems and the accuracy of the systems. So I'm actually going to go into one aspect of it that previous presenter talked about, but you can see that from this you really can't pick out those features that were looked at before such as the edge of pavement, gutter, face of curb or back of curb. So how to resolve those details? So I'm going to focus on looking at the beam divergence which most people are familiar with beam divergence as the ability for a beam to basically find those holes in vegetation, and put as much laser energy into that hole so you can get a return from the wet forest floor. But with respect to accuracy, beam divergence is sort of analogous to GSD for people who are familiar with photogrammetry methods. And it's a way to measure what is the resolution of that lighter point on the ground because often people think okay, I've got great density, but density doesn't necessarily equate to resolution when you're looking for those fine features. So basically as a laser goes farther, its beam starts to get bigger, and this is beam divergence. So there's an example of a small beam divergence and a larger beam divergence. So at the end at a longer range, smaller beam divergence means smaller spot, larger beam divergence, larger spot. And so when you look at the beam divergence for various different systems, on the far left, you've got a very small spot. So that's one of our systems at 0.3mR, and moving to the right is an example of beam divergence of several different systems on the market. The other thing is the beam divergence can really change based on or some types of hardware have beams that are not symmetric around the axis. And that means that when you're planning your flight, if you've got one of these systems that has basically the beam diverges on a different axis, how you plan your flight is all of a sudden going to become important. And sometimes you really don't have control over that. For example, when you're trying to fly beside a roadway and still scan onto the roadway. So let's look at an example of a large beam divergence and how that really affects basically trying to sample a curve. So when that large point hits the ground, it's first going to hit a spot away from the curve. And that's the spot that's actually going to start the response of the LIDAR time of flight. And so, but the thing is when you georeference the data, it's going to be georeferenced in the center of that spot. So that's where the spot is going to basically be recorded. Similarly, when you hit towards the top of the curve, it's going to hit there first and the spot registered there. And then lastly, the best case scenario is where you basically hit in the middle. And so for some people who have looked at similar data or you saw in the previous slide, this explains why that width of the curve is so wide in that data. Now when you take a smaller spot, you can see how now where it hits is where the point is going to get recorded. So when you then take a look at what you can get with basically a LIDAR scanner that's capable of collecting basically engineering grade data, this is an example of that. So you can pick up the edge of the pavement here. This is actually picked up using the intensity of the LIDAR, not the actual range measurement here. But you can clearly pick out the gutter where the face of the curve is and the back of the curve and the grass to the left of it. So then another example to look at is the wide or the road profiles. So here's an example from mobile mapping. And so when you're looking at the wider road profile for a road, there's the road slope so that or it can drain to the gutters. There's picking up the curbs and gutters on their own, picking up vegetation for that vegetation inventory. And on the side here, unfortunately, there was a fence there and that really blocked looking at the side of the road. This could be a fence, this could be a barrier. But if you look at what you would get from a UAS system, this is actually quite different. So you're still being able to pick up those features that are important. For example, the slope of the road, the median, the curbs and gutters that we were looking at before. The barriers that we have prevented a mobile mapping system from collecting this data. But in this case, we're able to get the embankment on the side too. So take for example, if you're in an area that was recently flooded or if you're just monitoring that embankment, which can road or change over time, you're not able to pick that up with a mobile mapping system. But with a UAS system, we can and simultaneously collect all the higher accuracy data from the road itself. So when you look at really what are the benefits that you get from basically doing road infrastructure surveys from an engineering grade system, there's really two that I like to focus on. The first is that we can capture both the topographic data and the engineering grade data in a single survey. So you're basically improving efficiency, less time in the area, less chance of error or having to manipulate different data sets together to get to the end deliverables. And lastly, you can basically capture that large area including the full corridor width. So for some projects that can be a couple hundred meters on either side of the data and you're able to collect that in an engineering survey. So again, significant productivity improvements in a single project. That's the end of my presentation here. If you've got any more questions, I'll be around afterwards. Or I'm in Hall 27 where we've got both our UAS LiDAR solution and our other LiDAR solutions. Thank you.