Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1

Bernard, Jérémy; Bocher, Erwan; Le Saux Wiederhold, Elisabeth; Leconte, François; Masson, Valéry

doi:https://doi.org/10.5194/gmd-15-7505-2022

Articles | Volume 15, issue 19

https://doi.org/10.5194/gmd-15-7505-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Benchmark datasets and machine learning algorithms for Earth...

https://doi.org/10.5194/gmd-15-7505-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 19

Model evaluation paper

|

11 Oct 2022

Model evaluation paper |

| 11 Oct 2022

Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1

Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson

Data sets

Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1) J. Bernard, E. Bocher, E. Le Saux Wiederhold, F. Leconte; V. Masson, and C. Noûs https://doi.org/10.5281/zenodo.6855063

Model code and software

GeoClimate: a Geospatial processing toolbox for environmental and climate studies Bocher; Bernard; Le Saux Wiederhold; Leconte; Petit; Palominos; Noûs https://doi.org/10.5281/zenodo.6372337

Short summary

OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.