Open Data Initiatives
Jump to navigation
Jump to search
Open Data Initiatives
Dataset | Country | Level of detail | CityGML ver. | Year | Acquisition technique | Other thematic classes | Textures | Notes |
---|---|---|---|---|---|---|---|---|
Berlin | Germany | LOD2 | 2.0 | 2013 | ? | Terrain | Yes | Released in 2015 |
Brussels | Belgium | LOD2 | 1.0 | 2014 | Building | No | EPSG:31370 | |
Hamburg | Germany | LOD1 and LOD2 | 1.0 | 2015 | Cadastre footprints + LiDAR | |||
Linz | Austria | LOD2 | 1.0 | 2011 | ? | No | ||
Lyon | France | LOD2 | 2.0 | 2012 | Terrain, water | |||
Montréal | Canada | LOD2 | 1.0 | 2009 | Photogrammetry | Yes | ||
New York City by TUM | United States | LOD1 | 2.0 | 2015 | Photogrammetry in combination with existing public 2(.5)D datasets | Roads, lots, parks, water, terrain | No | Very rich in attributes |
New York City by DoITT | United States | LOD2 (buildings are modeled with thematic surfaces in LOD2, however, for most buildings the geometric shape is LOD1) | 2.0 | 2016 | Cadastre footprints + LiDAR | No | ||
Nordrhein-Westfalen Open Data | Germany | LOD 1 and LOD2 | 2.0 | 2017 | Cadastre footprints + LiDAR | Building | No | urn:adv:crs:ETRS89_UTM32*DE_DHHN92_NH |
Potsdam | Germany | LOD2 | 1.0 | 2011 | Cadastre footprints + LiDAR | no | no | urn:ogc:def:crs,crs:EPSG:6.12:25833,crs:EPSG:6.12:5783 |
Rotterdam | Netherlands | LOD2 | 1.0 | 2010 | Cadastre footprints + LiDAR | Yes | ||
Den Haag Open Data | Netherlands | LOD2 | 1.0 | 2010 | Terrain | No | It contains terrain intersection curves | |
Thüringen | Germany | LOD1 and LOD2 | 1.0 | 2015 | no | no | urn:adv:crs:ETRS89_UTM32*DE_DHHN2016_NH | |
BuildZero Urban Data Model for the United States | United States | LOD1 | 2.0 | 2019 | no | no | EPSG:4979 |
If you are aware of open datasets which are not listed above please contact Filip Biljecki (NUS Singapore) or Karl-Heinz Häfele (KIT).
You might also be interested in sample CityGML data available on the CityGML website, datasets from the Netherlands automatically generated with 3dfier, and in procedurally generated CityGML models.
The quality of most of these dataset has been evaluated in a paper published in the proceedings of the 3D GeoInfo 2016 conference.