 Hello, welcome back on my YouTube channel. In this video, we are going to interpolate point cloud data. I'm loading the same data from the dunes in the Netherlands as we've used in a previous video. If we zoom in, we can see the individual points of the point cloud that are colored by the RGB colors of an aerial photograph. In the Layer Styling panel, I can also visualize the elevations by a ramp, which you see here. But these are points, and often we need rusters for further analysis. Let's see what tools we have available. I open the Processing Toolbox, and under Point Cloud Conversion, I find two Export to Ruster Tools. The first one uses IDW in first distance weighing, and the second one, Export to Ruster, using triangulation, uses a thin triangulation. Let's first check the IDW interpolator. As an input layer, I use the Point Cloud, and I choose the Elevation Attribute Z. I can change here the spatial resolution of the pixels, and I keep it at the default one meter. The nice thing is I can filter with an expression, choosing certain points, and I can crop the extent. And here I'm going to crop it to a smaller area, just for this demo. And I draw a box in the map canvas to choose a nice area for our demo. After it adds the extent to the dialog, I'm going to save the file to IDW1 for one meter. Later I will try different resolutions and show the effect. The running close the dialog, and let's inspect the result. By default it uses a single band grayscale renderer, but that's not very informative. So in the Layer Styling Panel, I'm going to use the Hill Shade, which is much better suited to check the result. We need to put the resampling zoomed into Billinear to avoid the blocky artifacts. And here we have the result. And if I switch on the Point Cloud Layer, I can also see that the geometry nicely fits the original layer. Now I'm going to repeat this for 50 centimeter pixels, 25 centimeter pixels, and 10 centimeter pixels, and to evaluate the result. You know, I also want to visualize all the layers with the same Hill Shade, so I'm going to copy the style, paste it to the other layers. Now let's visually compare the results. I zoom a bit in and we see already that the 10 centimeter result doesn't look very nice. 25 centimeters looks nicer than the 50 centimeters and the one meter. So it's all about how big the pixels are, but if we go to the 10 centimeter, we will see a lot of no data because the algorithm can't find points that are close. Which result in no data pixels, which could be interpolated further using the Fill No Data tool. Because 25 centimeter pixels gave the best results, I'm going to use the same spatial resolution for interpolation using tin or triangulation. This dialogue, I don't need to give the set attribute. It automatically uses that. And I'm going to use the same extent as we used in the 25 centimeter result from IDW. And I save the result to tin025.tiv. And I run the algorithm. Which can be a bit slower than the IDW interpolator, so that can be challenging if you have a very large data set. After running, click Close. And let's also paste the style there. So we have it in the Hill Shade. And for the zoomed out, I'm also going to use Billinear for both layers. And then we can compare them. We can easily visually compare the layers by switching them on and off. And for different areas and different zoom levels, we see there are some little differences. The IDW result seems to have a bit more artifacts. But the difference is minimal. But the better way of comparing them is using a side-by-side map window. And therefore I'm going to create here the map themes. The first one with only the tin layer, I call tin. And here the second one with IDW, I call IDW. And with map themes, I can easily switch between the two layers. So let's create some more space here. And I'm going to add a new map view, a 2D map view. And I'm going to make it as big as the first map. These maps are now not for a specific layer. So I choose tin on the right side. And then I'm going to synchronize the view with the center of the main map. And I'm going to synchronize the scale. In that way, I can zoom and pan and both windows are linked and I can compare the differences. We can also compare the interpolations using elevation profiles. There under view, you can go to elevation profile and this adds another panel. And I can draw a profile line in the map canvas. Click right and it will add the elevation values. But all the lines have the same color. So I'm going to double click and change the color of the line. And I'll do this for all the lines. Let's give the profile some more space by closing the other map view. I can change the y-axis by moving the panel bit up. I can pan the graph and I can zoom in the graph and I can switch on and off lines to compare. So here we have the tin 25 centimeters, the IDW 25 centimeters and the original points of point cloud. I'm going to change the point size, want it as a circle and I use purple. But here it still uses the colors from the point cloud itself, the RGB colors. So I need to uncheck that box, respect layers coloring. And I have my purple dots here. And then this way I can compare the result. As you see, it's quite minimal between tin and IDW. But if I go to the very coarse one meter result, the difference is big. If I go to the 10 centimeter result, you can see that although it fits nicer with the points, it has gaps. In this video, you've learned how to interpolate point clouds to rusters using two methods, IDW and triangulation. And you've learned how to compare the results using multiple 2D views and using elevation profile.