Most point clouds I’ve worked with have been tens of gigabytes in size.
In 2007, FME users saw point clouds representing one point per square meter for airborne data. In 2013, we saw eight points per square meter for the same data. Today, it's more like eighty points per square meter, and LiDAR is used to scan everything from landscapes to movie props.
So how do you use this massive amount of data to your advantage? Below are 7 ways we’ve seen people make the most out of this versatile data type.
Much like CAD, GIS, rasters, and other data types, LiDAR data is not limited to a single format. Point clouds might be delivered as LAS (compressed or not), XYZ, ASTM E57, Oracle Spatial, Pointools POD, RIEGL, TerraScan, ZFS, or any number of other formats. Even Minecraft is technically just a big, blocky point cloud.
A magical chasm opens in spacetime when you combine LiDAR data with other data types. Whether that’s CAD, GIS, rasters, vector data, 3D geometries, another point cloud, a reconstructed pirate map, or all of the above, your point cloud data just got interesting.
That multi-gigabyte beast on your hard drive has no hope of transforming into a handsome prince if you can’t decipher it. A key step in any transformation is inspection, so open your point clouds for viewing to find out what components are involved.
With the right inspection tool, even the most enormous point cloud datasets will start to make sense.
LiDAR data has come to us in a variety of coordinate systems (UTM, StatePlane, etc). If you need to project the point cloud onto a map or combine it with other data, you can reproject it to another system just like any other type of spatial data.
Tiling is when you chop the input features (points) into a series of tiles. If you have a big point cloud and a lot of transformations to do, try tiling it and using parallel processing. It’ll make your old transformation time look like a sloth on sedatives. Of course, tiling is also useful if you need to generate smaller pieces of the point cloud for delivery.
1.2 billion points isn’t a crazy number when dealing with LiDAR data. In fact, the word “billion” is pretty common. Chances are, you might not need all of those points in your output or analysis. Maybe you only want a specific region, like the area around a street.
Clipping is when you toss away the points outside of a defined boundary. This can be mega-helpful in creating a manageable size to work with.
If you provide a 3D solid as your clipper shape, you can perform a cubic clip.
Maybe you want a manageable size to work with, but you don’t want to clip out important features and make the earth look like it got crushed by a giant steamroller. Thinning a point cloud reduces its overall volume; for example, by removing every Nth point.
Point clouds are a highly useful, advanced way to store data. As the popularity of LiDAR continues to grow, we will certainly see more and more ways to transform this data type.