Landscape Position using GDAL — PT 3

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

Landscape Position using GDAL

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

FOSS4G Korea 2014, poor GPS photos, and mapillary (part 2 of n)

A classic and age old problem in GPS is collecting potentially wonderful data in the field, getting back the office, and realizing a lot of manual scrubbing, data massaging, and other such careful work will need to be done to make the GPS data useful and meaningful. This assumes we can even meaningfully correct it at all. This is true too (maybe especially) for GPS … Continue reading FOSS4G Korea 2014, poor GPS photos, and mapillary (part 2 of n)

KNN with FLANN and laspy, a starting place

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

LiDAR and pointcloud extension pt 5

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

LiDAR and pointcloud extension pt 3

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