Automatic Extraction 3D Building Models from UAV LiDAR Point Cloud Data: A Case Study of Tunjungan Street, Surabaya

High resolution 3D City Models are essential for the Smart City paradigm, yet acquiring accurate spatial data in dense tropical urban environments remains challenging due to the limitations of passive optical sensors. This study addresses these issues by validating a semi-automated workflow for generating Level of Detail 2 (LOD2) building models using UAV LiDAR data, specifically focusing on mitigating systematic strip misalignment errors. Using Surabaya’s Tunjungan Street as a case study, the research implements a rigorous strip adjustment method followed by nDSM-based extrusion guided by 2D footprints. Results demonstrate that strip adjustment is indispensable, improving Z-axis consistency by nearly 50% (from 1.23 mm to 0.63 mm). Crucially, this improvement minimizes vertical discrepancies and outliers, ensuring the high data cohesion required for precise architectural reconstruction and preventing geometric misinterpretations. The final products achieved a Horizontal Accuracy (CE90) of 0.079 m and Vertical Accuracy (LE90) of 0.385 m, surpassing Indonesian Class 1 standards for 1:5,000 scale mapping. Furthermore, the LOD2 models exhibited a vertical RMSE of 0.268 m, confirming the workflow's reliability for precision critical urban planning.