Automatic DEM-infused 2D to 3D LoD1 Urban Morphology Python Framework

Abstract. The generation of 3D urban morphology models from 2D urban morphology maps has been widely explored. Traditional methods use modelling software, such as Rhino, which lack georeferencing, elevation, and automation. In this study, we developed an open-source Python framework for automatic generation of 3D city blocks, including elevation, from 2D colour-graded building heightmaps and urban morphology input. We utilised the UT-GLOBUS and GlobalBuildingAtlas building datasets to generate heightmaps and retrieved other urban morphology features, such as waterbodies, parks, roads, and trees, from OpenStreetMap to form the input raster patches. The framework generates height and colour maps based on the input features, which are extruded in 3D and exported into multiple standard 3D GIS formats such as CityGML and CityJSON. Six global cities: Sydney, New York, London, Rio de Janeiro, Hong Kong, and Singapore, were modelled to demonstrate the framework’s applicability. Validation includes qualitative comparison with Google Earth 3D data and quantitative comparison against official LiDAR-derived DSMs for four cities. Quantitative results show moderate height errors and good spatial agreement of building footprints, reflecting the expected differences between simplified LoD1 block models and detailed DSM representations. Our framework results show promising potential in the field of 2D to 3D mapping for the creation of 3D city models for urban climate modelling and environmental analysis. The generated 3D models can be downloaded at https://doi.org/10.5281/zenodo.17620303.