Garret glw

PostGIS topology operator	equivalent GraphQL operator
`ST_Contains`	`_st_contains`
`ST_Crosses`	`_st_crosses`
`ST_Equals`	`_st_equals`
`ST_Intersects`	`_st_intersects`
`ST_Overlaps`	`_st_overlaps`
`ST_Touches`	`_st_touches`
`ST_Within`	`_st_within`
`ST_DWithin`	`_st_d_within`

Install dlib and face_recognition on a Raspberry Pi

Instructions tested with a Raspberry Pi 2 with an 8GB memory card. Probably also works fine on a Raspberry Pi 3.

Steps

Download the latest Raspbian Jessie Light image. Earlier versions of Raspbian won't work.

Write it to a memory card using Etcher, put the memory card in the RPi and boot it up.

This tutorial is partialy based on Yuriy’s Czoli article ‘Processing LiDAR to extract building heights’.

Install PostGIS

On Mac OSX

Install Postgres, PostGIS and OSM-PostGIS tools

brew install postgis

PostgreSQL & PostGIS Cheatsheet

This is a collection of information on PostgreSQL and PostGIS for what I tend to use most often.

Processing LiDAR to extract building heights

Walk through

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

	import logging
	import urllib.request
	import boto3
	from settings import OBJECT_STORAGE_KEY, OBJECT_STORAGE_SECRET, OBJECT_STORAGE_REGION, OBJECT_STORAGE_BUCKET

	logger = logging.getLogger(__name__)

	s3config = {
	"region_name": OBJECT_STORAGE_REGION,
	"endpoint_url": "https://{}.digitaloceanspaces.com".format(OBJECT_STORAGE_REGION),

	# An example to get the remaining rate limit using the Github GraphQL API.

	import requests

	headers = {"Authorization": "Bearer YOUR API KEY"}


	def run_query(query): # A simple function to use requests.post to make the API call. Note the json= section.
	request = requests.post('https://api.github.com/graphql', json={'query': query}, headers=headers)
	if request.status_code == 200:


	<!DOCTYPE html>
	<html>
	<head>
	<meta charset=utf-8 />
	<title>WaterHex</title>
	<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no' />
	<script src='https://api.tiles.mapbox.com/mapbox.js/v1.6.2/mapbox.js'></script>
	<link href='https://www.mapbox.com/base/latest/base.css' rel='stylesheet' />
	<link href='https://api.tiles.mapbox.com/mapbox.js/v1.6.2/mapbox.css' rel='stylesheet' />