LoD1 3D City Model from volunteered public data and a raster Digital Elevation Model.

We generate a Level-of-Detail 1 (LoD1) 3D City Model (buildings and terrain) from OpenStreetMap (osm) contributions with elevation from a raster Digital Elevation Model (DEM).

There are two procesing strategies

Village/Campus	District [This is on hold]
If your Area-of-Interest (aoi) has less than 2 500 buildings, you are welcome to choose village/campus	Please choose District if your aoi has more than 2 500 buildings

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Input a raster DEM. Script will call for the osm contributions.
Output includes:

     i. a topologically correct LoD1 City Model *(information rich building models seperate from the ground; but when connected to the terrain form a water-tight surface^*)*;      ii. one use case of 3D city models. Population estimation and the calculation of [Building Volume per Capita](https://www.researchgate.net/publication/343185735_Building_Volume_Per_Capita_BVPC_A_Spatially_Explicit_Measure_of_Inequality_Relevant_to_the_SDGs); and      iii. an interactive .html which you can navigate and query. ^* *the goal is a model that conforms to the ISO 19107 standard [connecting and planar surfaces, correct orientation of the surfaces and watertight volumes]. I have not tested this for all possibilities. If the result you achieve is not; you are welcome to raise an issue. I depend on you to help me improve.*     [Documentation](https://adriankriger.github.io/osm_LoD1_3DCityModel/) is available. The [announcement](https://github.com/AdrianKriger/osm_LoD1_3DCityModel/discussions/22#discussion-4781443) is noteworthy. **For a quickstart** *(and to see the value and usefulness of a 3D City Model)*: ```mermaid flowchart LR A[osm_LoD1_3DCityModel-walkthrough.ipynb] -->B(CityJSONspatialDataScience.ipynb) B --> D[Population estimation]; B --> E[Building Volume per Capita]; B --> F[Interactive visualization]; ``` ---

Paper currently under review with the South African Journal of Geomatics

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