3D Solar Potential Analysis Results for Munich based on CityGML Semantic City Models

Solar Potential Analysis Munich – Zenodo SummaryAuthors This dataset was produced by the Chair of Geoinformatics, Technical University of Munich (TUM) and the Leibniz Institute of Ecological Urban and Regional Development (IÖR), Dresden. Project Context The data was generated within the project "Solarpotenzialanalyse und Web-Visualisierung von 3D-Geomassendaten am Beispiel der Stadtregion München", initiated by the Landesamt für Digitalisierung, Breitband und Vermessung (LDBV) and the Runder Tisch GIS e.V. (RTG). The project aimed at analyzing building-integrated photovoltaic (BIPV) potential across the entire Munich metropolitan area using 3D geodata. Analysis Tool The solar potential analysis was performed using a Java-based tool developed by Bruno Willenborg, Maximilian Sindram, and Thomas H. Kolbe at the Chair of Geoinformatics, TUM. It computes direct and diffuse solar irradiation as well as the Sky View Factor (SVF) on LoD2 CityGML building surfaces using a ray-casting approach, considering shadowing from surrounding buildings, vegetation, and terrain models. The tool is calibrated with 22-year mean values from the NASA SSE dataset. The vegetation model used in the "with trees" scenario is based on the LiDAR-Based Tree Models for Munich dataset by Münzinger (2025). It was derived from airborne LiDAR point clouds using an object-based data-fusion approach that integrates multispectral aerial imagery and a semantic 3D building model to classify and parametrize individual trees (height, crown diameter, trunk height, position). The resulting semantic 3D tree models correspond to CityGML LoD2 and were integrated into the city model as shading objects for the solar analysis. Data Content The package contains results for the entire city of Munich in two scenarios: ● With trees – vegetation model derived from LiDAR point clouds included as shading objects: m_2.2_a_t1-25_100.7z ● Without trees – vegetation excluded from shading calculation: m_2.2_t1-25_100.7z Both scenarios share the same settings: ● Tiling size: 100 × 100 m ● Terrain models: DGM1 (near range) and DGM25 (far range, 20 km buffer) ● Building model: LoD2 CityGML Each scenario contains the following deliverables: ● Textured CityGML models with solar irradiation mapped as building textures (color scale: blue = low irradiation, red = high irradiation) for yearly global irradiation ● Sampling point cloud (CSV) with per-point monthly aggregated results ● glTF visualization models for use with the 3DCityDB Web Map Client. See the documentation of the 3DCityDB Webmap Client to learn how to configure it for the visualization of the tiled glTF files. CSV Data Description The sampling point cloud CSV files use a semicolon delimiter and contain the following fields per record: ● id – Point identifier ● surfacetype – CityGML surface type (e.g. RoofSurface, WallSurface) ● thematic_id – Numeric thematic surface identifier ● thematic_gmlid – GMLID of the thematic surface ● building_id – Numeric building identifier ● bldg_gmlid – GMLID of the building as found in the CityGML LoD2 dataset. ● month – Month of the year (1–12). ● area_surface – Area of the parent surface (m²) ● area_point – Representative area of the sampling point (m²). Each point stands representative for a part of the building surface. Use this value to multiply with the values for directrad, diffuserad, globalrad to get the solar irradiation energy for the area represented by this 3D point. In order to determine the solar energy for an entire surface of the 3D building model for a specific month, sum up the values mentioned before for all points belonging to the same thematic surface for the respective month. ● directrad – Direct solar irradiation (kWh/m²). The data was originally computed for each 3D point for every hour of every day of the respective month and then aggregated for that month. ● diffuserad – Diffuse solar irradiation (kWh/m²). The data was originally computed for each 3D point for every hour of every day of the respective month and then aggregated for that month. ● globalrad – Global solar irradiation (kWh/m²). The data was originally computed for each 3D point for every hour of every day of the respective month and then aggregated for that month. ● svf – Sky View Factor (0–1). 0 means no part of the sky is visible from the respective 3D point. 1 means 100% of the sky dome above the scene is visible. Sky dome means a hemisphere arching over and above the scene. ● diffuseamount – Diffuse fraction flag ● x – X coordinate, Easting according to EPSG:25832 (ETRS89 datum, UTM zone 32N) ● y – Y coordinate, Northing according to EPSG:25832 (ETRS89 datum, UTM zone 32N) ● z – Z coordinate, Height according to EPSG:7837 (DHHN2016 vertical reference system) References ● Münzinger, M. (2025): LiDAR-Based Tree Models for Munich, Germany (2022). ioerDATA, V1. https://doi.org/10.71830/CNC4VU ● Münzinger, M., Prechtel, N., Behnisch, M. (2022). Mapping the urban forest in detail: From LiDAR point clouds to 3D tree models. Urban Forestry & Urban Greening, 74, 127637. https://doi.org/10.1016/j.ufug.2022.127637 ● Willenborg, B., Sindram, M., Kolbe, T. H. (2018). Applications of 3D City Models for a better Understanding of the Built Environment. In: Behnisch, M., Meinel, G. (eds): Trends in Spatial Analysis and Modelling, Geotechnologies and the Environment, Berlin, Heidelberg: Springer International Publishing, 167–191. https://doi.org/10.1007/978-3-319-52522-8_9 ● Yao, Z., Nagel, C., Kunde, F., Hudra, G., Willkomm, P., Donaubauer, A., Adolphi, T., Kolbe, T. H. (2018). 3DCityDB – a 3D Geodatabase Solution for the Management, Analysis, and Visualization of Semantic 3D City Models based on CityGML. Open Geospatial Data, Software and Standards, 3(5), 1–26. ● Chair of Geoinformatics, Technical University of Munich: https://www.asg.ed.tum.de/gis/startseite/ ● Leibniz Institute of Ecological Urban and Regional Development (IÖR): https://www.ioer.de/ ● Project Geomassendaten: https://www.asg.ed.tum.de/gis/aktuelles/article/kooperationsprojekt-geomassendaten-erfolgreich-abgeschlossen/ ● NASA Langley Research Center (2016): Surface meteorology and Solar Energy (SSE) Data and Information. https://eosweb.larc.nasa.gov/project/sse/sse_table ● 3DCityDB Web Map Client: https://github.com/3dcitydb/3dcitydb-web-map