TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset

TL;DR

TUM2TWIN is a comprehensive multimodal dataset designed to advance the development and validation of Urban Digital Twins by providing diverse, georeferenced 3D models and sensor data for urban environment analysis.

Contribution

This paper introduces the first large-scale, multimodal Urban Digital Twin benchmark dataset, enabling comprehensive validation and development of UDT-related methods.

Findings

Supports advanced reconstruction methods like NeRF and Gaussian Splatting

Enables analysis of solar potential and semantic segmentation

Facilitates high-accuracy, multimodal urban data integration

Abstract

Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple process stages, including acquiring accurate 3D source data, reconstructing high-fidelity 3D models, maintaining models' updates, and ensuring seamless interoperability to downstream tasks. Current datasets are usually limited to one part of the processing chain, hampering comprehensive UDTs validation. To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN. This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m^2$ and currently 767 GB of data. By ensuring georeferenced indoor-outdoor acquisition, high accuracy, and multimodal data integration, the benchmark supports robust analysis of sensors and the development of advanced reconstruction methods. Additionally, we explore downstream tasks demonstrating the potential of TUM2TWIN, including novel view synthesis of NeRF and Gaussian Splatting, solar potential analysis, point cloud semantic segmentation, and LoD3 building reconstruction. We are convinced this contribution lays a foundation for overcoming current limitations in UDT creation, fostering new research directions and practical solutions for smarter, data-driven urban environments. The project is available under: https://tum2t.win