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
(In which Steve vehemently disagrees with a major thought leader on the nature of Civic Technology… .) (TLDR: Open Data is the platform. Governance participates in, contributes to, and benefits from Open Data, but government is not the platform, Open Data is the platform.) I’m sorry, Tim O’Reilly. You are a brilliant man, have some truly great ideas, and some successes I hear as a … Continue reading Government 2.0 is not the platform
It is probably apropos that the week after I left Korea was a holiday everyone in Seoul described as “Korean Thanksgiving” or “Asian Thanksgiving”. This brief post is a celebration of some of the elements of thanksgiving that occur to me as I return from Ohio GIS conference, after recent trips to FOSS4G and FOSS4G Korea. No matter what I do, this will be too … Continue reading Thanksgiving
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
Digging a little deeper. Ran the chipper on a smaller number of points and then am doing a little hacking to get height per chip (if you start to get lost, go to Paul Ramsey’s tutorial). Here’s my pipeline file. Note the small chipper size– 20 points per chip. Easy enough to load (though slow for the sake of the chip size): Now we can … Continue reading LiDAR and pointcloud extension pt 3
As much for my edification as anyones: Want to get at just a few points within the pointcloud extension? A with query will get you there: Continue reading LiDAR and pointcloud extension pt 2
Paul Ramsey has a great tutorial on using the pointcloud extension with PostgreSQL / PostGIS: http://workshops.boundlessgeo.com/tutorial-lidar/ You can get point cloud and all that goodness running a variety of ways. Probably the easiest is to download OpenGeo Suite from Boundless: http://boundlessgeo.com/solutions/opengeo-suite/download/ If you are an Ubuntu user, try a docker instance to run PostGIS with PDAL, pointcloud, etc in a container: https://github.com/vpicavet/docker-pggis Also, I’m working … Continue reading LiDAR and pointcloud extension
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