Population data for Germany is available on a high-resolution 100 m grid from the census 2022. By comparing this data to buildings, one can find areas in Germany that are not yet mapped in OpenStreetMap. These are mostly farms, weekend homes and some new construction.
The identified unmapped areas are fed as mapping tasks into Maproulette, a micro-tasking platform for OpenStreetMap contributors, where they can improve the map one small issue at a time.
An earlier version of this project was run in 2020, see tag v1.0 of this repository.
- German Census 2022: Population on 100 m grid (“Bevölkerungszahlen in Gitterzellen”). Keep in mind that the data is based on the cut-off date 2022-05-15 and things may have changed since then or the latest construction is not reflected in the data.
- OpenStreetMap dump for Germany from Geofabrik
01_download.sh – Download data
Download the input data.
02_createdb.sh – Create database
Create the PostGIS database where the data analysis will happen.
03_import_census.sh – Import census data
Import the data into the PostGIS database.
The data looks like this:
04_import_osm.sh – Import OSM data
Filter the OpenStreetMap data for buildings and some other relevant land uses. OpenStreetMap contains all kinds of geospatial data, e.g. roads, shops and schools. We are only interested in areas where people live like residential areas or buildings. Some other types of places need to be included like nursery homes, hospitals, prisons or military barracks, where people may reside permanently.
On this map you can see how these areas overlap with the census cells. We are interested in the cells which are not yet covered by residential areas.
05_analyze.sh – Intersect the data sets
Now we filter the census cells to include only those that are not covered or touched in any way by the relevant OSM objects. These identify areas that should be looked at for sure.
Finally identify clusters of touching squares and merge them into single polygons to identify each connected area that should become one task.
This map shows how the final output looks like:
Each one of the polygons is then presented as mapping task to the Maproulette contributors. The outline indicates the rough area to check, but users will then use satellite/aerial imagery to get the precise outline.
You can run the analysis yourself, e.g. for newer data or if you want to modify the criteria.
The processing requires about 100 GB of temporary disk space and 1 hour of computation time.
The easiest way to get all required dependencies and run the pipeline is to use
podman. It should be readily available as package on
recent Linux distributions. If you have podman installed you can run
podman play kube kube.yamlThis will first build the image defined in unmapped_census/ which contains all the required dependencies.
It starts this image and PostgreSQL+PostGIS as two containers and runs run.sh, which just calls the scripts 01 to 06.
The generated output data can be found in data/unmapped_census.geojson.
Install PostgreSQL, PostGIS, osm2pgsql and osmium.
Edit env.sh to set the PostgreSQL credentials.
Run ./run.sh to execute all processing steps, or call the single scripts to
run specific steps.
The source code of this project is licensed under the terms of the MIT license.
The census data may be used for derivative works, if you mention the source “Statistisches Bundesamt (www.destatis.de)”. See their imprint for details.
As the output data is a Derivative Work of OpenStreetMap data, is has to be licensed under ODbL. Please refer to the OSM Copyright page and the information linked there.