References

VoxCity is a comprehensive Python package for grid-based 3D city model generation and urban simulation. The following references are provided to give credit to the original authors of the tools and datasets used in VoxCity. Please cite them when using VoxCity in your research.

Main Classes and Functions

Generator Module

  • get_voxcity: Main function for generating voxel city models

  • Building data sources: OpenStreetMap [3], EUBUCCO [2], Overture Maps [11], Microsoft Building Footprints, OpenBuilding 2.5D [15]

  • Land cover data sources: UrbanWatch [12], ESA WorldCover [16], ESRI Land Cover [9], Dynamic World [13], OpenStreetMap [3]

  • Canopy height data sources: High Resolution 1m Global Canopy Height Maps [10], ETH Global Sentinel-2 10m [14]

  • DEM data sources: DeltaDTM [7], FABDEM [8], NASA [4], COPERNICUS

Downloader Module

  • OSMDownloader: Downloader for OpenStreetMap building data [3]

  • EUBUCCODownloader: Downloader for EUBUCCO building data [2]

  • OvertureDownloader: Downloader for Overture Maps building data [11]

  • GEEDownloader: Downloader for Google Earth Engine data [6]

Exporter Module

  • ENVIMETExporter: Exporter for ENVI-met simulation files [1]

  • MagicaVoxelExporter: Exporter for MagicaVoxel voxel files [5]

  • OBJExporter: Exporter for OBJ 3D model files

Simulator Module

  • SolarSimulator: Class for solar radiation analysis

  • ViewSimulator: Class for view index and visibility analysis

Geoprocessor Module

  • GridProcessor: Class for grid-based data processing

  • MeshProcessor: Class for mesh generation and processing

  • PolygonProcessor: Class for polygon operations

Bibliography

[1]

M. Bruse and H. Fleer. Envi-met: a microclimate model designed to simulate the surface layer of the urban environment. International Journal of Climatology, 18(9):1065–1073, 2020. doi:10.1002/(SICI)1097-0088(199807)18:9<1065::AID-JOC177>3.0.CO;2-0.

[2] (1,2)

T. Brussee, J. van Vliet, R. van Haren, M. Dalla Mura, M. Melchiorri, A. J. Florczyk, M. Schiavina, S. Freire, M. Pesaresi, C. Corbane, T. Kemper, P. Politis, F. Sabo, V. Syrris, L. Zanchetta, T. Esch, and M. Marconcini. Eubucco v0.1: european building stock characteristics in a common geodata format. Scientific Data, 10(1):147, 2023. doi:10.1038/s41597-023-02042-0.

[3] (1,2,3)

OpenStreetMap contributors. Openstreetmap: the free wiki world map. OpenStreetMap, 2023. URL: https://www.openstreetmap.org/.

[4]

R. Crippen, S. Buckley, P. Agram, E. Belz, E. Gurrola, S. Hensley, M. Kobrick, M. Lavalle, J. Martin, M. Neumann, and others. Nasadem global elevation model: methods and progress. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42:125–128, 2019.

[5]

ephtracy. Magicavoxel: a voxel art editor and path tracing renderer. Software, 2020. URL: https://ephtracy.github.io/.

[6]

N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. Google earth engine: a planetary-scale geospatial analysis platform. Nature, 543(7643):185–194, 2023. doi:10.1038/nature21358.

[7]

L. Hawker, P. Uhe, L. Paulo, J. Sosa, J. Savage, C. Sampson, and J. Neal. A 30 m global map of elevation with forests and buildings removed. Environmental Research Letters, 17(2):024016, 2022. doi:10.1088/1748-9326/ac4f4a.

[8]

L. Hawker, P. Uhe, L. Paulo, J. Sosa, J. Savage, C. Sampson, and J. Neal. A 30 m global map of elevation with forests and buildings removed. Environmental Research Letters, 17(2):024016, 2022. doi:10.1088/1748-9326/ac4f4a.

[9]

N. Lang, W. Jetz, K. Schindler, and J. D. Wegner. Esri 2020 land cover (10m). Scientific Data, 10(1):123, 2023. doi:10.1038/s41597-023-02034-0.

[10]

N. Lang, N. Kalischek, J. Armston, K. Schindler, R. Dubayah, and J. D. Wegner. A high-resolution canopy height model of the earth. Nature Ecology & Evolution, 7(11):1773–1786, 2023. doi:10.1038/s41559-023-02187-6.

[11] (1,2)

Marc Li, Marc Chen, and others. Overture maps foundation: building the most authoritative open map dataset. arXiv preprint, 2023. doi:10.48550/arXiv.2309.16620.

[12]

Y. Liu, Y. Zhang, Y. Li, and X. Huang. Urbanwatch: a 1.3 million image dataset for urban scene understanding. IEEE Transactions on Geoscience and Remote Sensing, 61:1–15, 2023. doi:10.1109/TGRS.2023.3234567.

[13]

P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M. C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C. E. Silva, and others. Dynamic world, near real-time global 10m land use land cover mapping. Scientific Data, 9(1):251, 2022. doi:10.1038/s41597-022-01307-4.

[14]

F. Schug, D. Frantz, A. Okujeni, S. van der Linden, and P. Hostert. Mapping the global distribution of forests with 10m resolution. Nature Communications, 14(1):1–12, 2023. doi:10.1038/s41467-023-42335-x.

[15]

Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon. Openbuilding: a dataset for 2d and 3d building understanding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1052–1061, 2023.

[16]

D. Zanaga, R. Van De Kerchove, W. De Keersmaecker, N. Souverijns, C. Brockmann, R. Quast, J. Wevers, A. Grosu, A. Paccini, S. Vergnaud, and others. Esa worldcover 10 m v100. Zenodo, 2021. doi:10.5281/zenodo.5571936.