 This is Jonathan Yildon with the University of Vermont Spatial Analysis Lab, and in this video I'll be covering some basics of high resolution feature extraction using imagery in LiDAR. I'll make use of a few software packages in this video tutorial, including eCognition, QuickTrainModeler, and ArcGIS. There are two datasets we'll be using in this tutorial, an ortho-photo in imagined format, and a LiDAR point cloud dataset in LAS format. Let's look at the image dataset within ArcGIS. If we go into the properties you'll notice that this is a digital ortho-photo that has four bands associated with it. We're symbolizing it in ArcGIS as a 412 RGB composite, or a color infrared composite. In QuickTrainModeler, we're going to open up that LAS LiDAR point cloud dataset. This particular LiDAR dataset also has intensity data so we can toggle on and off the vertex colors. In addition, this LAS file has been classified by the contractor. Of greatest interest to us for feature extraction is the fact that ground points, shown here in green, have been separated from non-ground points. If we go into the properties of the LiDAR dataset, two key factors of our keen interest to us. One, the scale, which is approximately spacing between points, and density, which is a number of points per square unit, in this case square meters. Now we're going to remove the LiDAR point cloud file, and we're going to reload the data as a gridded surface model. We're going to set the grid sampling to a value of one for one meter, and we're going to the gridding options. Here we're going to click on a help. First, we're going to set our gridding options to create a DEM or digital elevation model. In order to do this, we'll want to ensure that we're only importing the ground points. By clicking on the class.