In PDAL, a pipeline file can be used to do a variety of operations. Within the following context, I think of a pipeline file like an ad hoc preferences file, where I can use an external command to iterate through the things I want to change, while holding constant everything else in the pipeline file. In my use case for this vignette, I’ll use the … Continue reading Taking Slices from LiDAR data: Part IV
forest_structure Borrowed from: http://irwantoshut.com Continuing my series on slicing LiDAR data in order to analyze a forest, one of the objectives of the current project is to understand the habitats that particular species of birds prefer. This will be accomplished using field info from breeding bird surveys combined with LiDAR data of forest structure to help predict what habitats are necessary for particular species of … Continue reading Taking Slices from LiDAR data: Part III
Wow. Thanks to my colleague Brandon Garman for this tip — Point Clouds in Sketchup. Mind you, I don’t think this is an inexpensive proposition, but a pretty cool looking tool: Continue reading Point Clouds in Sketchup?
Ok, with a little help from Bradley Chambers on the PDAL mailing list, we are back in business. If we want to filter our newly calculated heights into a new PDAL output, we can do that easily, say all points 100-500 above ground level: A little sanity check to see if we are getting appropriate values: Ok, now I want to view this. I could convert … Continue reading Taking Slices from LiDAR data: Part II
My current project to create a complete dataset of airborne LiDAR data for Ohio and Pennsylvania has been teaching me some simple, albeit really powerful tricks. We’ll just discuss one today — recursive use of wget. This allows us to download entire trees of web sites to mirror, or in our case download all the data. Additionally, wget works on ftp trees as well, with … Continue reading wget for downloading boatloads of data
PDAL now has the capacity to calculate heights from your point cloud data. With pre-classified LiDAR data, this means you can do this pretty easily: A problem you might have is you may not have all the wonderful PDAL goodness built and installed. So you might get something like this: An easy way around this is to let docker do all the work. Once the … Continue reading PDAL and point cloud height
If you have followed this blog for a while, you will notice a continual returning to and refinement of ideas and topics. That’s how the blog started, and this role it has served, as a touch stone in my exploration of topics is critical to how I use it. I hope it is useful to you too, as a reader. So, let’s talk about point … Continue reading Point Clouds – the (re)start of a journey
FLANN is Fast Library for Approximate Nearest Neighbors, which is a purportedly wicked fast nearest neighbor library for comparing multi-dimensional points. I only say purportedly, as I haven’t verified, but I assume this to be quite true. I’d like to move some (all) of my KNN calculations outside the database. I’d like to do the following with FLANN– take a LiDAR point cloud and change … Continue reading KNN with FLANN and laspy, a starting place
There’s all sorts of reasons why what I’ve been doing so far is wrong (see the previous 5 posts). More on that later. But in case you needed 3D visualization of the chipping I’ve been working through, look no further: Continue reading LiDAR and pointcloud extension pt 6
Now for the crazy stuff: The objective is to allow us to do vertical and horizontal summaries of our data. To do this, we’ll take chipped LiDAR input and further chip it vertically by classifying it. First a classifier for height that we’ll use to do vertical splits on our point cloud chips: And now, let’s pull apart our point cloud, calculate heights from approximate … Continue reading LiDAR and pointcloud extension pt 5
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