CityLODer: City models from airborne point clouds

Airborne point clouds provide a rich but unstructured description of urban environments, making their direct use in visualization, simulation, and geospatial applications difficult in non-trivial use-cases. This paper presents CityLODer , a automatic and scalable pipeline for converting raw data into usable 3D city representations with a unified procedure. Building on the earlier LiD2LOD system, CityLODer broadens the reconstruction process to a more comprehensive urban representation that includes both buildings and roads. The tool produces semantic CityGML Level of Detail 1 models, together with lightweight triangular meshes. Particular attention is given to robustness, ease of use, and automation, allowing the pipeline to process large airborne datasets with limited manual intervention. We demonstrate the system on historical urban scenarios characterized by irregular layouts and complex morphology, showing that CityLODer can generate compact, semantically structured, and visually effective city models from raw point-cloud data.