4D spatial–temporal information infrastructure for digital twin environments using Spatial IDs

Digital twin technologies are gaining global attention as foundational components for data-driven infrastructure management and urban simulation. However, significant challenges remain in integrating and utilizing geospatial datasets, including inconsistencies in spatial referencing methods, the absence of semantic object identification, and alignment issues due to positional inaccuracies. This study proposes a 4D spatial–temporal information infrastructure leveraging a voxel-based spatial referencing method known as Spatial IDs. The infrastructure enables unified spatial indexing across heterogeneous datasets and supports high-speed data retrieval, dynamic distribution, and automatic object identification from point cloud data. Key system components include a binary voxel-based search index, a data compression and distribution mechanism using LASzip format, and a semantic segmentation module that integrates map-based object annotations with point cloud geometry. Demonstration experiments involving city-scale digital twin construction, drone route interference detection, and object identification confirmed the infrastructure's efficiency and scalability. The proposed infrastructure reduced retrieval time to under one second and achieved high object identification accuracy (F1-score ≥ 0.99), illustrating its applicability in real-time geospatial analytics and intelligent urban systems.