“Frankfurt Airport tunnel” by Peter Isotalo – Own work. Licensed under CC BY-SA 3.0 via Commons. In my ongoing quest to process all the LiDAR data for Pennsylvania and Ohio into one gigantic usable dataset, I finally had to break down and learn how to do parallel processing in BASH. Yes, I still need to jump on the Python band wagon (the wagon is even … Continue reading parallel processing in PDAL
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
More landscape position pictures — just showing riparianess. See also https://smathermather.wordpress.com/2014/11/22/landscape-position-using-gdal/ and https://smathermather.wordpress.com/2014/11/24/landscape-position-using-gdal-pt-2/ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL Continue reading Landscape Position using GDAL — PT 3
Just pretty pics today of estimated riparianess. If you prefer a bit of code, see previous post https://smathermather.wordpress.com/2014/11/22/landscape-position-using-gdal/ Continue reading Landscape Position using GDAL — PT 2
Hat tip again to Seth Fitzsimmons. I’ve been looking for a good, easy to use smoothing algorithm for rasters. Preferably something so easy, I don’t even need to write a little python, and so efficient I can run it on 30GB+ datasets and have it complete before I get distracted again by the next shiny project (a few hours). Seth’s solution? Downsample to a low … Continue reading Landscape Position using GDAL
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
Now, we navigate into unknown (and perhaps silly) territory. Now we do point level calculations of heights, and drop the new calculated points back into the point cloud as though it were a Z value. I don’t recommend this code as a practice– this is as much as me thinking aloud as anything Caveat emptor: Continue reading LiDAR and pointcloud extension pt 4
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