| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <body> | |
| <script src="http://d3js.org/d3.v3.min.js"></script> | |
| <script src="http://d3js.org/topojson.v1.min.js"></script> | |
| <script> | |
| var width = 960, | |
| height = 720, | |
| origin = [71, -42], |
Running LAStools on your Mac
If you want to use LAStools on OS X you will have to do some prep. This is a brief introduction to get you up and running with LAStools on a Mac in the Terminal.
You may have heard that you should use wine to run LAStools on OS X. Depending on your experience this might sound intuitive, or like utter jibberish. For those who empathize with the latter, let's walk through this.
Homebrew
Yesterday the government of Mexico launched datos.gob.mx to open up government data across all ministries. We [built a mapping tool](blog post) for ministries to quickly build rich maps from data on the site. The tool also makes it to combine government data with other open datasets. This provides context and meaning to complex government data.
The first dataset that we mapped was all 2013 funds for disaster response and reconstruction. The map plots thousands of reconstruction projects across 45 natural disasters, including Hurricane Manual and Ingrid which affected two-thirds of Mexico, killing 192 people and causing $75 billion pesos in damage.
This is an incredibily rich and complex dataset. But this data alone is not particularly helpful. We need to better understand the context to understand why government invested funds the w
| #!/bin/bash | |
| echo "Enter bands (ie 4 3 2)" | |
| read B1 B2 B3 | |
| echo "Enter the resulting filename" | |
| read outputname |
| #!/bin/bash | |
| echo "Enter bands in descending order, (ie 4 3 2)" | |
| read B1 B2 B3 | |
| echo "Enter the resulting filename" | |
| read outputname |
The National Oceanic and Atmospheric Administration (NOAA). Great for coastal regions/cites. Includes lidar coverage in certain areas.
http://www.coast.noaa.gov/dataviewer/#app=b1ed&bda3-selectedIndex=1
U.S. Geological Survey (USGS). Their earth explorer has a wide variety of data sets including satellite imagery, satellite radar (SRTM), and pockets of lidar coverage.
| #!/usr/bin/env python | |
| # ndvi.py red.tif nir.tif output-ndvi.tif | |
| # Calculate NDVI (see Wikipedia). Assumes atmospheric correction. | |
| # (Although I use it without all the time for quick experiments.) | |
| import numpy as np | |
| from sys import argv | |
| from osgeo import gdal, gdalconst |
tl;dr
This is the scrappy technical overview of processing the Southern Ontario 1954 imagery. I assume you are familiar with gdal, bash scripting, raster imagery. If you are trying to process similar data and something is unclear, feel free to reach out to me with questions.
I first found the data here:
Detailed walk through of building extraction using postgis
First lets pull a data layer from of openstreetmap. You can do this any which way you’d like, as there are a variety of methods for pulling openstreetmap data from their database. Check the [wiki] (http://wiki.openstreetmap.org/wiki/Downloading_data) for a comprehensive list. My favourite method thus far is pulling the data straight into QGIS using the open layers plugin. For those who may want to explore this method, check [this tutorial] (http://www.qgistutorials.com/en/docs/downloading_osm_data.html). For building extraction you only need building footprints, and include the building tags. Not all polygons are of type building in OSM, so we can download all the polygons, and then filter the layer for only polygons tagged as buildings.
LiDAR data was pulled from USGS via the Earth Explorer site. [Here] (http://earthobservatory.nasa.gov/blogs/ele