Geoscience Australia 1:250 000 GIS Datasets

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Many Geoscience Australia (GSA) datasets are available under a Creative Commons Attribution 4.0 International Licence license and are permitted to be imported into OpenStreetMap under the ODbL. The 1:250 000 GIS datasets also falls under the scope of datasets made available through the extensive collection of open data. Permission was also explicitly requested for this particular dataset.


This is a series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats. The large number of maps means this particular dataset is an ongoing OSM community import.

Of particular interest to OpenStreet Mappers:

  • Names for populated places (cities, towns, localities, etc) and places (islands, mountains, straits, etc)
  • Vegetation areas including native vegetation (bushland) and cultivated areas (land which is actively in use for forestry plantations).


In edit descriptions, the statement: © Commonwealth of Australia (Geoscience Australia) 2016 should be used when specifying a GSA source, attribution is also found on the Contributors page.

How to obtain the data

This is filtered search of datasets through, datasets can be identified by their name format: <region name> 1:250 000 GIS Dataset. They can also be directly obtained from Geoscience Australia.

General import guidelines

A lot of this data has already been added to OpenStreetMap via surveys, aerial imagery and other imports - especially around more densely populated regions. As always, great care should be taken to avoid duplicating or overwriting existing data.

Since OSM already has names assigned for many larger populated places, the place names obtained from this GSA dataset are most useful for "filling in" any gaps - most often rural and remote areas. Categorisation of place values must be performed with Bing imagery since the dataset provides no information of the relative importance of places (such as distinguishing between an unpopulated locality or a hamlet).

Vegetation polygons should be simplified to reduce the number of nodes (JOSM 12m simplification should be sufficient while maintaining an acceptable level of detail). Due to the age of the dataset (circa 2006), a quick verification of vegetation boundaries with Bing imagery should be undertaken due to any deforestation which may have occurred in the years since.