Tegola OSM

This repo houses instructions and configuration files to aid with standing up an OpenStreetMap export and Natural Earth dataset into a PostGIS enabled database that uses tegola for creating and serving vector tiles.

Dependencies

If you want to use these scripts you will need the following:

• Postgres server with PostGIS enabled.
• imposm3 (download - linux only)
• tegola (download)
• gdal - required for Natural Earth import

Basic overview

The scripts in this repo prepare 2 databases with data from 3 sources:

1. OpenStreetMap - highly detailed data for roads, rail, buildings, rivers, lakes, and more.
2. Natural Earth - data for country borders, state lines, land, major roads, and more.
3. OpenStreetMap Land Polygons - high detail polygons for landmass.

To import all this data into your databases the scripts use Imposm3 for OpenStreetMap data (1) and Gdal for both NaturalEarth and OSM land polygon data (2+3).

Once the data has been imported it is ready to serve with Tegola.

Repo config files

The following files allow you to configure what data is imported into your databases and how the data is served by Tegola:

• imposm3.json - an imposm3 mapping file for the OSM PBF file.
• tegola.toml - a tegola configuration file for how to serve the OSM data created by Imposm.

Getting started

Step 1. Prepare databases

Create two databases called osm and natural_earth and enable the extensions postgis and hstore on both of them.

createdb -E utf8 -O my_pg_user osm
psql -d osm -c "CREATE EXTENSION postgis;"
psql -d osm -c "CREATE EXTENSION hstore;"

createdb -E utf8 -O my_pg_user natural_earth
psql -d natural_earth -c "CREATE EXTENSION postgis;"
psql -d natural_earth -c "CREATE EXTENSION hstore;"

The steps below will import OSM (1) and OSM land (3) data into "osm" and Natural Earth (2) data into "natural_earth".

Step 2. Download your desired OSM dataset in PBF format

Since processing map data can be time-consuming it's best to start with a city rather than the whole planet. You can download OSM data for individual cities at Geofabrik. For this guide, we will use London.

In this repo's root directory run the following:

• curl -O 'http://download.geofabrik.de/europe/great-britain/england/greater-london-latest.osm.pbf'

Step 3. Import the OSM export into PostGIS using Imposm3

You will use Imposm to map the data from the OSM dataset (1) into your osm database. Imposm requires you to do this in 2 steps.

• ./imposm3 import -connection postgis://your_pg_user:your_password@localhost/osm -mapping imposm3.json -read ./greater-london-latest.osm.pbf -write

• ./imposm3 import -connection postgis://your_pg_user:your_password@localhost/osm -mapping imposm3.json -deployproduction

The osm database now has all the OSM data (1) ready for use.

Step 4. Import the OSM Land and Natural Earth dataset

Now you need to add the OSM land polygon data (3) to the osm database. Update the following lines in the osm_land.sh script with your database details e.g.

DB_NAME="osm"
DB_HOST="localhost"
DB_PORT="5432"
DB_USER="your_pg_user"
DB_PW="your_password"

The Natural Earth data will be imported into the natural_earth database you created earlier. Update the same lines in the natural_earth.sh script with the relevant details. E.g.

DB_NAME="natural_earth"
DB_HOST="localhost"
DB_PORT="5432"
DB_USER="your_pg_user"
DB_PW="your_password"

Then run each file: ./natural_earth.sh && ./osm_land.sh.

This will download the natural earth and osm land datasets and insert them into PostGIS under your natural_earth and osm databases respectively.

Note: For debugging options and more advanced ways to complete this step see "Alternative ways to import the OSM Land and Natural Earth dataset" below.

Step 5. Install SQL helper functions

Execute postgis_helpers.sql against your OSM database. Currently, this contains a single utility function for converting building heights from strings to numbers which is important if you want to extrude buildings for the 3d effect.

• psql -U your_pg_user -d osm -a -f postgis_helpers.sql

Step 6. Setup SQL indexes

Execute postgis_index.sql against your OSM database.

• psql -U your_pg_user -d osm -a -f postgis_index.sql

Step 7. Launch Tegola

• ./tegola serve --config tegola.toml

Open your browser to localhost and the port you configured Tegola to run on (i.e. localhost:8080) to see the built-in viewer.

Alternative ways to import the OSM Land and Natural Earth dataset

Step 4 took a simple approach to configure the osm_land.sh and natural_earths.sh scripts by simply having you hard code them with your DB credentials. However, there are two other options this step can be accomplished by which might suit your needs better in production environments.

Option 2: Create a dbcredentials.sh file

Create a dbcredentials.sh file which will be shared with the osm_land script. This option is ideal for when the natural_earth and osm databases will reside on the same database server, and will use the same credentials. Ensure that the following variables are defined in your file:

DB_HOST="mydbhost"
DB_PORT="myport"
DB_USER="myuser"
DB_PW="mypassword"

Once you have configured the dbcredentials.sh file, run the scripts as above:

• ./natural_earth.sh && ./osm_land.sh

Option 3:

Create separate configuration files in the same pattern as the above dbcredentials.sh file and pass the path to the config file using the -c option. This is ideal if you have two different servers for the databases. Ensure the file you create follows this format:

DB_NAME="mydb"
DB_HOST="mydbhost"
DB_PORT="myport"
DB_USER="myuser"
DB_PW="mypassword"

Once you have configured the files, run the scripts with the -c flag and provide the path to the credentials file, ie:

• ./natural_earth.sh -c natural_earth_creds.sh && ./osm_land.sh -c osm_creds.sh

Advanced Usage

Both scripts support a -v flag for debugging. natural_earth.sh also supports a -d flag, which will drop the existing natural earth database prior to import if set. Since the osm_land.sh imports into the osm database which is shared with other data, it lacks this functionality. Instead, only the relevant tables are dropped.

Data Layers

To view these data layers in a map and query the features for a better understanding of each data layer, use the Tegola-OSM Inspector. The data layers described here are in the "Tegola-OSM" database as laid out in the tegola.toml (i.e., not the Natural Earth database that is specified in tegola-natural-earth.toml).

source	Description
ne	Natural Earth data, version 4
osm	OpenStreetMap data, current
osm land	OpenStreetMap-derived land polygons from openstreetmapdata.com, currentness depends on last pull

Note: All layers also have the data fields: layer id and geometry. An empty where column means that all features are retained.

populated_places

points

zoom	source	table/layer	data fields	where
0-2	ne	ne_110m_populated_places	scalerank, labelrank, name, min_zoom, featurecla, rank_max
3-4	ne	ne_50m_populated_places	scalerank, labelrank, name, min_zoom, featurecla, rank_max
5-20	ne	ne_10m_populated_places	scalerank, labelrank, name, min_zoom, featurecla, rank_max

country_lines

zoom	source	table/layer	data fields	where
0-2	ne	ne_110m_admin_0_boundary_lines_land	featurecla, name, min_zoom
3-4	ne	ne_50m_admin_0_boundary_lines_land	featurecla, name, min_zoom
5-10	ne	ne_10m_admin_0_boundary_lines_land	featurecla, name, min_zoom

country_lines_disputed

lines

zoom	source	table/layer	data fields	where
3-4	ne	ne_50m_ne_50m_admin_0_boundary_lines_disputed_areas	featurecla, name, min_zoom
5-10	ne	ne_10m_ne_50m_admin_0_boundary_lines_disputed_areas	featurecla, name, min_zoom

country_label_points

zoom	source	table/layer	data fields	where
3-20	ne	ne_10m_admin_0_label_points	sr_subunit, scalerank

country_polygons

zoom	source	table/layer	data fields	where
0-2	ne	ne_110m_admin_0_countries	featurecla, name, name_long, abbrev, adm0_a3, min_zoom, min_label, max_label
3-4	ne	ne_50m_admin_0_countries	featurecla, name, name_long, abbrev, adm0_a3, min_zoom, min_label, max_label
5-10	ne	ne_10m_admin_0_countries	featurecla, name, name_long, abbrev, adm0_a3, min_zoom, min_label, max_label

state_lines

zoom	source	table/layer	data fields	where
0-2	ne	ne_110m_admin_1_states_provinces_lines	featurecla, name, adm0_name, min_zoom
3-4	ne	ne_50m_admin_1_states_provinces_lines	featurecla, name, adm0_name, min_zoom
5-10	ne	ne_10m_admin_1_states_provinces_lines	featurecla, name, adm0_name, min_zoom

land

polygons

zoom	source	table/layer	data fields	where
0-2	ne	ne_110m_land	featurecla, min_zoom
3-4	ne	ne_50m_land	featurecla, min_zoom
5-7	ne	ne_10m_land	featurecla, min_zoom
8-20	osm land	land_polygons

admin_lines

zoom	source	table/layer	data fields	where
8-12	osm	admin_boundaries_8-12	admin_level, name, type	admin_level IN (1,2,3,4,5,6,7,8)
13-20	osm	admin_boundaries_13-20	admin_level, name, type	admin_level IN (1,2,3,4,5,6,7,8,9,10)

state_label_points

zoom	source	table/layer	data fields	where
3-20	ne	ne_10m_admin_1_label_points	name, scalerank

landuse_areas

Nature reserves, military land, forest, leisure, wood, etc. polygons

zoom	source	table/layer	data fields	where
3-5	osm	landuse_areas_gen0	name, class, type, area	type IN ('forest','wood','nature reserve', 'nature_reserve', 'military') AND area > 1000000000
6-9	osm	landuse_areas_gen0_6	name, class, type, area	type IN ('forest','wood','nature reserve', 'nature_reserve', 'military') AND area > 100000000
10-12	osm	landuse_areas_gen1	name, class, type, area
13-20	osm	landuse_areas	name, class, type, area

water_areas

polygons

zoom	source	table/layer	data fields	where
3-5	osm	water_areas_gen0	name, class, type, area	type IN ('water', 'pond', 'basin', 'canal', 'mill_pond', 'riverbank') AND area > 1000000000
6-9	osm	water_areas_gen0_6	name, class, type, area	type IN ('water', 'pond', 'basin', 'canal', 'mill_pond', 'riverbank') AND area > 100000000
10-12	osm	water_areas_gen1	name, class, type, area	type IN ('water', 'pond', 'basin', 'canal', 'mill_pond', 'riverbank') AND area > 1000
13-20	osm	water_areas	name, class, type, area	type IN ('water', 'pond', 'basin', 'canal', 'mill_pond', 'riverbank', 'dock')

water_lines

zoom	source	table/layer	data fields	where
8-12	osm	water_lines_gen0	name, type	type IN ('river', 'canal')
13-14	osm	water_lines_gen1	name, type	type IN ('river', 'canal', 'stream', 'ditch', 'drain', 'dam')
15-20	osm	water_lines	name, type	type IN ('river', 'canal', 'stream', 'ditch', 'drain', 'dam')

transport_lines

Roads, airport runways, ferry routes, paths, etc.

zoom	source	table/layer	data fields	where
3-4	ne	ne_10m_roads_3	name, min_zoom, min_label, type, label	min_zoom < 5 AND type <> 'Ferry Route'
5-6	ne	ne_10m_roads_5	name, min_zoom, min_label, type, label	min_zoom <= 7 AND type <> 'Ferry Route'
7-8	osm	transport_lines_gen0	type, tunnel, bridge, ref	type IN ('motorway','trunk','motorway_link','trunk_link','primary') AND tunnel = 0 AND bridge = 0
9-10	osm	transport_lines_gen1	ref, class, type	type IN ('motorway', 'trunk', 'primary', 'primary_link', 'secondary', 'motorway_link', 'trunk_link')
11-12	osm	transport_lines_11-12	name, ref, class, type, tunnel, bridge, access, service	type IN ('motorway', 'motorway_link', 'trunk', 'trunk_link', 'primary', 'primary_link', 'secondary', 'secondary_link', 'tertiary', 'tertiary_link', 'rail', 'taxiway', 'runway', 'apron')
13	osm	transport_lines_13	name, ref, class, type, tunnel, bridge, access, service	type IN ('motorway', 'motorway_link', 'trunk', 'trunk_link', 'primary', 'primary_link', 'secondary', 'secondary_link', 'tertiary', 'tertiary_link', 'rail', 'residential', 'taxiway', 'runway', 'apron')
14-20	osm	transport_lines_14-20	name, ref, class, type, tunnel, bridge, access, service

transport_areas

Airports, etc. polygons

zoom	source	table/layer	data fields	where
12-20	osm	transport_areas	name, class, type

transport_points

Airports, helipads, etc.

zoom	source	table/layer	data fields	where
14-20	osm	transport_points	name, class, type

amenity_areas

Fire stations, banks, embassies, government, police stations, schools, universities, etc. polygons

zoom	source	table/layer	data fields	where
14-20	osm	amenity_areas	name, type

amenity_points

Fire stations, banks, embassies, government, police stations, schools, universities, etc.

zoom	source	table/layer	data fields	where
14-20	osm	amenity_points	name, type

other_points

Man made, historic, military, barriers, power towers, etc.

zoom	source	table/layer	data fields	where
14-20	osm	other_points	name, class, type

other_lines

Man made, historic, military, barriers, power lines, etc.

zoom	source	table/layer	data fields	where
14-20	osm	other_lines	name, class, type

other_areas

polygons Man made, historic, military, power, barriers, piers, etc.

zoom	source	table/layer	data fields	where
6-8	osm	other_areas_filter	name, class, type	area > 1000000
9-20	osm	other_areas	name, class, type

buildings

polygons

zoom	source	table/layer	data fields	where
14-20	osm	buildings	name, height, type

How long does it take to import the entire planet?

If you run this import, please send in a PR to report your import machine specs and how long it takes.

@peldhose: 11.30 hours on a Google cloud server with 8 vCPU, 30GB RAM and 1TB storage (400GB used)
@SahAssar 15.43 hours on a Dell XPS 13 9380 i7-8565U 16GB RAM and 1TB SSD (375GB used by postgres after import)