A material-aware, multi-modal synthetic dataset for smart city applications: Design and construction

Large-scale synthetic environments are essential for advancing smart cities research. However, existing urban benchmarks typically prioritize either visual photorealism or geometric fidelity, often neglecting the material-level semantics and physical attributes required for simulations. To address this gap, this paper introduces UR-MAT , a modular design framework and a multimodal synthetic dataset specifically built to bridge computer vision and physics-based modelling. We present a unified construction workflow that integrates georeferenced geometry, physically based rendering (PBR) materials, and domain-specific metadata (e.g., electromagnetic properties based on ITU-R standards) into a traceable data representation. The resulting dataset encompasses eight heterogeneous urban scenarios, providing spatially aligned RGB imagery, depth maps, semantic segmentation masks, and 3D point clouds. The utility of UR-MAT is validated through cross-domain experiments: (i) a quantitative baseline analysis on semantic material segmentation across fourteen material classes, where the best-performing model achieved 0.787 mIoU and 0.968 pixel accuracy, and (ii) electromagnetic ray-tracing simulations, which verify that the dataset’s material annotations yield physically consistent and deterministic radio propagation patterns. By enabling perception and simulation tasks within a single coherent framework, UR-MAT serves as a resource for developing perception-driven communication strategies and next-generation Digital Twins.