High-resolution Digital Elevation Models (DEMs) are essential for urban flood modelling, where small elevation differences govern drainage and inundation extent. However, DEMs frequently contain hydrological inconsistencies: bridges, tunnels and culverts appear as artificial barriers disrupting flow continuity, while flood defence structures may be poorly represented at the available resolution. This paper presents an automated open-source Python pipeline for generating hydrologically conditioned DEMs by integrating classified airborne LiDAR data with OpenStreetMap (OSM) infrastructure features. The workflow is tested on a 16 km² area over Copenhagen city centre using a 2023 national LiDAR acquisition (13.5 pts/m²). A 0.5 m resolution DSM is generated from LiDAR ground and building classes via Inverse Distance Weighting (k=12, power=2, max radius 5 m), with Nearest Neighbour gap-filling. Hydrological conditioning applies four sequential operations: bridge burning at 108 footprints, tunnel enforcing at 53 shallow underpasses, culvert enforcing at 8 subsurface passages, and barrier rasterization raising 199 flood defence structures to their LiDAR-measured top-of-wall elevations. In total, 198,733 pixels were lowered (median 0.69 m) and 27,022 raised (median 3.00 m above ground), reducing the trapped depression volume in the DSM by 718,496 m³ (−5.0%). Vertical accuracy is assessed against the Danish national terrain model DHM/Terræn (NMAD = 0.066 m, LE90 = 0.265 m). The conditioned DEM feeds the CLEAR-EO urban flood simulation chain at the Danish Meteorological Institute; full hydraulic validation is outside the scope of this work. The pipeline is modular and transferable to other urban contexts with pre-classified LiDAR and OSM data.