OpenStreetMap is a free and editable map of the world.  Founded in 2004 in the United Kingdom in response to the need for a free geospatial data source, it is a community-driven project, allowing for anyone to edit and contribute information.   It has since grown to include data from almost all countries in the world.  The map generated from their data can be used as an alternative to commercial map layers such as those found in Google Maps.  The OpenStreetMap base layer is rendered using Mapnik, however since the data is free to download and distribute, it is possible to serve it using GeoServer.   I recently went through the process of rendering OpenStreetMap’s data with GeoServer.

The data

OpenStreetMap provides instructions on downloading their data.  The full data set is a large file, currently about 4GB when compressed and about 100GB when uncompressed.  (You can download sections of the data set as well.)  This data is an XML-based data format that GeoServer cannot read natively.  PostGIS is the optimal way of storing such a large volume of data when using GeoServer.  Luckily, there is a converter that allows you to load the OpenStreetMap data into PostGIS called osm2pgsql.  This program is a package available on Debian-based distributions (such as Ubuntu), and is also available as a binary on Windows.

Run the following command:

osm2psql -E 900913 -d osm planet.osm

This will process the XML information and load the data into a PostGIS database called “osm”. The -E defines the projection of the source data, which in this case is 900913, the projection used in Google Maps, and the default for OpenStreetMap.

If successful, the database will contain the following tables:

planet_osm_line planet_osm_point planet_osm_polygon planet_osm_roads

Making sense of the data

OpenStreetMap’s data can be a bit confusing.  For a first time user like myself, just trying to figure out their naming convention was challenging.  They do, however, have a wonderful map key hidden away in their wiki.  This page is a lifesaver.

There are two different tables that contain line data, planet_osm_line and planet_osm_roads. The former includes railroads, subways, and other linear information.  The latter is made up exclusively of roads.  The planet_osm_point table has a range of data: subway stations, shopping centers, universities, and even brothels. Lastly the planet_osm_polygon table has, but is not limited to, parks, bodies of water, and even buildings in certain urban areas.


On my map I grouped the roads into three major groups; residential, secondary, and limited-access (highways).  To achieve this grouping I used the following SLD filters.

**Road class** **PropertyName** **Values**
Residential highway residential OR unclassified
Secondary highway primary OR secondary
Limited-access highway motorway OR trunk

I then styled the map to distinctly display the three groups of roads, and also varied their widths depending on the zoom level.  This was the most time-consuming part of the process, as I needed to create and evaluate styles for each zoom level.  The final SLD is very large, but is available for those interested.

Final touches

Since the purpose of this map was to create a viable base layer that anyone can incorporate into their mapping projects, performance was a concern.  To address this, I used GeoWebCache, a tile cache mechanism built into GeoServer.  The GeoWebCache documentation has details on this process.

The finished (?) map

The finished product can be found at

Feel free to link to this map.  Use this OpenLayers code to get started.  This map is a work in progress.  Future enhancements will include adding public transportation systems, railroads, buildings, and more. If you have any feedback on this map please email me at iwillig at opengeo dot org.

With GeoServer, PostGIS, OpenLayers and the OpenStreetMap data set, it is possible to build high-quality, professional-looking maps, allowing you to take control of what data you present, define your own mapping aesthetic, and free yourself from having to use commercial map layers.