Developing Complete Urban Digital Twins in Busy Environments: A Framework for Facilitating 3d Model Generation From Multi-Source Point Cloud Data

Abstract. The proliferation of affordable LiDAR technology and photogrammetry sensors has revolutionized 3D data acquisition in built environments, enabling comprehensive data capture from citywide scales to interior structures. This data can be transformed into digital twins, providing valuable resources for city planners, architects, engineers, and decision-makers. However, current studies often overlook the limitations of real-world point cloud datasets derived from LiDAR systems, which are voluminous, noisy, incomplete, and lacking information, which hinders monitoring, interpretation, and automated analysis. To address these challenges, methods are required to prepare point cloud data, ensuring accurate and reliable 3D representations. This research proposes a detailed framework for point cloud data preparation in busy urban environments. It includes precise algorithms, software, and parameter guidelines, allowing for the creation of comprehensive point cloud datasets. The framework has been successfully implemented on datasets acquired in Toronto, converting point cloud data from various platforms and parameters into an integrated dataset. Results demonstrate the framework's effectiveness in producing accurate and complete point cloud datasets for applications such as classification, information extraction, 3D model generation, and smart cities' monitoring and management.