DOI: 10.5281/zenodo.15557227
title: UR-MAT: A Multimodal, Material-Aware Synthetic Dataset of Urban Scenarios

description: 🏙️ URMAT: URban MATerials Dataset

URMAT (Urban Materials Dataset) is a large-scale, multimodal synthetic dataset designed for training and benchmarking material-aware semantic segmentation, scene understanding, and electromagnetic wave simulation tasks in complex urban environments.

The dataset provides pixel-wise annotated images, depth maps, segmentation masks, physical material metadata, and aligned 3D point clouds, all derived from realistic 3D reconstructions of urban scenes including Trastevere, CityLife, Louvre, Canary Wharf, Bryggen, Siemensstadt, and Eixample.

🧱 Key Features





14 material classes: Brick, Glass, Steel, Tiles, Limestone, Plaster, Concrete, Wood, Cobblestone, Slate, Asphalt, Plastic, Gravel, Unknown.




Multimodal data: RGB, depth, material masks, mesh segmentation




Physically annotated metadata: includes permittivity, reflectance, attenuation




7 diverse European city districts, georeferenced and stylistically accurate




Precomputed point clouds for 3D analysis or downstream simulation




Compatible with Unreal Engine, PyTorch, and MATLAB pipelines



📁 Dataset Structure

At the root of the dataset:





*_mapping/ folders: mapping files, mesh metadata, camera poses




*_pointclouds/ folders: colored 3D point clouds with material labels




train/, val/, test/: standard splits for training and evaluation



Inside each split (train/, val/, test/):








Folder Name
Description




rgb/
RGB images rendered from Unreal Engine


depth_png/
Depth maps as grayscale .png (normalized for visualization)


depth_npy/
Raw depth arrays saved as .npy


segmentation_material_png/
Color-encoded material segmentation masks for visualization


segmentation_material_npy/
Material masks in .npy format (integer IDs per pixel, for training)


segmentation_mesh/
Optional masks identifying the mesh origin of each pixel


metadata/
JSON metadata with material type and physical properties per mesh






 




📦 Recommended Use Cases





Material-aware semantic segmentation




Scene-level reasoning for 3D reconstruction




Ray tracing and wireless signal propagation simulation




Urban AI and Smart City research




Synthetic-to-real generalization studies



📜 Citation

If you use URMAT v2 in your research, please cite the paper:

UR-MAT: A Multimodal, Material-Aware Synthetic Dataset of Urban Scenarios@inproceedings{10.1145/3746027.3758314,author = {Russo, Debora and Mazzocca, Nicola and Vittorini, Valeria},title = {UR-MAT: A Multimodal, Material-Aware Synthetic Dataset of Urban Scenarios},year = {2025},isbn = {9798400720352},publisher = {Association for Computing Machinery},address = {New York, NY, USA},url = {https://doi.org/10.1145/3746027.3758314},doi = {10.1145/3746027.3758314},pages = {13164–13169},numpages = {6},location = {Dublin, Ireland},series = {MM '25}}

Debora Russo, Nicola Mazzocca, and Valeria Vittorini. 2025. UR-MAT: A Multimodal, Material-Aware Synthetic Dataset of Urban Scenarios. In Proceedings of the 33rd ACM International Conference on Multimedia (MM '25). Association for Computing Machinery, New York, NY, USA, 13164–13169. https://doi.org/10.1145/3746027.3758314