Abstract. The increasing demand for accurate 3D urban models is driven by rapid urbanization and the need for advanced geospatial tools in smart cities and digital twin applications. However, integrating Digital Terrain Models (DTMs) with 3D city models remains a persistent challenge due to misalignment between building footprints and terrain surfaces. Existing methodologies suffer from elevation discrepancies, inefficient interpolation techniques, and computational limitations, leading to inaccuracies in urban simulations, particularly for flood risk assessment and infrastructure planning. This study proposes a dual-iteration morphometric algorithm to enhance DTM-3D building integration, ensuring precise alignment of urban structures with real-world topography. The methodology involves node-based terrain adjustments, vertex and planar interpolation, and an iterative refinement process including land lot and building footprints geometry that improves elevation conformity. The algorithm was applied to a case study in Section 13, Petaling Jaya, Malaysia, using LiDAR-derived elevation data for validation. The results demonstrate a reduction in Root Mean Square Error (0.387 to 0.112) and a 75.8% decrease in Mean Absolute Error (0.219 to 0.053) compared to conventional DTM models, indicating significantly improved terrain adherence. Hydrodynamic simulations further reveal that the refined DTM reduces flood overestimation errors by 205.49%, capturing localized elevation variations more accurately. These improvements facilitate more reliable flood modelling, infrastructure planning, and urban resilience strategies. This research advances geospatial modelling for smart cities and digital twins, offering a scalable, high-accuracy framework for urban planning, disaster risk management, and climate adaptation. The proposed algorithm enhances urban simulation fidelity, ensuring more precise decision-making in sustainable city development.
Maulud et al. (Thu,) studied this question.