Also see the original Pieter Noordhuis's guide
You need:
- Raspberry Pi Model B (or B+) with a MicroSD Card $35-40
- An RTL-SDR dongle:
<?php | |
trait EnhancedEnum | |
{ | |
/** | |
* Get the enum value from the name. e.g case INVOICE = 'invoice'; will return 'invoice' | |
* | |
* @param string $name | |
* @return static | |
*/ |
--- | |
created: <% tp.file.creation_date() %> | |
--- | |
tags:: [[+Daily Notes]] | |
# <% moment(tp.file.title,'YYYY-MM-DD').format("dddd, MMMM DD, YYYY") %> | |
<< [[Timestamps/<% tp.date.now("YYYY", -1) %>/<% tp.date.now("MM-MMMM", -1) %>/<% tp.date.now("YYYY-MM-DD-dddd", -1) %>|Yesterday]] | [[Timestamps/<% tp.date.now("YYYY", 1) %>/<% tp.date.now("MM-MMMM", 1) %>/<% tp.date.now("YYYY-MM-DD-dddd", 1) %>|Tomorrow]] >> | |
--- |
cd /path-to-dir/LUREF_NGL/; find . -type f -iname '*.tif' >input-files.txt | |
docker run -it --rm -v /path-to-dir/LUREF_NGL:/data osgeo/gdal:alpine-normal-v2.4.1 sh -l | |
(in docker) gdalbuildvrt -resolution highest -r nearest ANA_LUREF_NGL_DTM.vrt -input_file_list input-files.txt | |
# Rest not in docker... | |
gdaladdo -ro --config COMPRESS DEFLATE --config COMPRESS_OVERVIEW DEFLATE --config ZLEVEL 9 --config BIGTIFF_OVERVIEW IF_SAFER --config GDAL_TIFF_OVR_BLOCKSIZE 512 -r nearest ANA_LUREF_NGL_DTM.vrt 4 16 64 256 1024 4096 | |
gdaldem hillshade ANA_LUREF_NGL_DTM.vrt lu_hillshade_2017.tif -co BIGTIFF=YES -co TILED=YES -co COMPRESS=DEFLATE -co GDAL_NUM_THREADS=ALL_CPUS -of GTiff -b 1 -z 1.0 -s 0.5 -az 315.0 -alt 45.0 | |
gdaladdo -ro --config COMPRESS_OVERVIEW JPEG --config PHOTOMETRIC_OVERVIEW YCBCR --config INTERLEAVE_OVERVIEW PIXEL --config BIGTIFF_OVERVIEW IF_SAFER --config GDAL_TIFF_OVR_BLOCKSIZE 512 -r average lu_hillshade_2017.tif 4 16 64 256 1024 4096 | |
gdalwarp -t_srs epsg:3857 -r lanczos -multi -wo NUM_THREADS=ALL_CPUS A |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
""" | |
Return GeoJSON centroids with population for each locality in Luxembourg. | |
BD-Adresses doesn't include communes, so we use the OpenStreetMap community's | |
csventrifuge output, which has been enriched. The output of this script is | |
© OpenStreetMap Contributors, see https://openstreetmap.org/copyright. |
Also see the original Pieter Noordhuis's guide
You need:
-- The following lines must be added to /usr/local/Cellar/osm2pgsql/HEAD/share/osm2pgsql/default.style | |
-- # Extras for create_osm_street_list.sql | |
-- way postal_code text linear | |
-- way alt_name text linear | |
-- way name:lb text linear | |
-- way alt_name:lb text linear | |
-- way is_in:city text linear | |
DROP TABLE IF EXISTS road_names_osm; |
Technical details for https://stackoverflow.com/a/44169445/6730571
On a base system, /usr/bin/java
is a symlink that points to /System/Library/Frameworks/JavaVM.framework/Versions/Current/Commands/java
, which is an Apple wrapper tool that locates and executes the actual java
.
(Do not touch anything in those 2 system directories. It should actually be impossible due to "System Integrity Protection" anyway.)
If you don't have Java installed, attempting to execute java
will open a dialog that invites you to install it.
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