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
We’ve got some quick and dirty pgRouting-based code up on github. I say quick and dirty because it directly references the table names in both of the functions. I hope to fix this in the future. The objective with this code is to input a point, use a nearest neighbor search to find the nearest intersection, and from that calculate a drive time alpha shape. … Continue reading Drivetime analyses, pgRouting
(A guest blog post from Dakota Benjamin today, a Case Western Reserve University senior, and 3 year Summer intern we’ve been luck enough to recruit) This post has two big take-aways: using spatial data in R, and applying that knowledge to estimating home ranges. If you are not familiar with the R environment, there are many great resources to familiarizing yourself with this powerful language, … Continue reading Using Spatial Data in R to Estimate Home Ranges (guest blog post)
We’ve got two wheels now, baby! (and I’ve refined this a bit from a bungee only setup). Here’s my rig for bicycling for Mapillary photos. This uses the same gimbal for stabilizing and leveling the photos, so it should have similarly great output. Mapillary output yet to come for this one. In the mean time, you can watch the camera gimbal come alive: Also you … Continue reading Bicycling for Mapillary
I really dig the crowd-sourced street-view service, Mapillary, and like to contribute to it, but doing a good job of getting blur-free images is a little tricky. This is double true in dark, under forest canopy trails. So, we solved it with some technology. Enter, a GoPro camera, a gimbal for an RC copter (drone), and some old bicycle parts: The gimbal is a two … Continue reading Unicycling for Mapillary
Want to load your favorite tiles into QGIS? How about a plugin-free QGIS TMS tiles via GDAL: http://www.3liz.com/blog/rldhont/index.php?post/2012/07/17/OpenStreetMap-Tiles-in-QGIS Really awesome… . Needs but one change: epsg:900913 should be epsg:3857 or QGIS (GDAL?) throws an error. Presumably you could also define epsg:900913 in some config file, but barring that create an XML file as follows, and load as a raster in QGIS: Now I can use … Continue reading Plugin-free QGIS TMS tiles via GDAL
Much the problem of the modern era– too much data, uneven data, and yet, should we keep it all? Here’s the problem space: attach GPS collar to a coyote, send that data home, and you have a recipe for gleaning a lot of information about the movement of that animal across the landscape. In order to maximize the data collected while also maximizing the battery … Continue reading Cleaning animal tracking data — throwing away extra points
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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