Welcome to the UDTIRI Benchmark Suite

Introduction

The conceptualization of the smart city through digital twins is evident. From urban planning to land-use optimization, it has the power to govern the city effectively. Urban Digital Twins (UDT) - defined as the application of digital twin technology to cities - is recognized as an opportunity to upgrade urban planning and develop smart cities. UDT technologies are expected to offer new opportunities for current transportation infrastructures and systems in terms of their evaluation and maintenance and will make such systems intelligent and self-sustaining in the future.

Intelligent Road Inspection (IRI) is becoming increasingly important due to a drastic increase in the number of vehicles and consequently road usage. The success of a road transport system is inherently dependent on the riding quality and comfort level of the users, for which timely detection of faults and ensuing maintenance are of utmost importance. The current manual observation and detection methods are cumbersome, time-consuming, and expensive. Therefore, in order to warrant long-standing structural integrity and safety levels, future transportation maintenance systems need to integrate innovative technologies that will employ next-generation distributed sensors and vision-based artificial intelligence approaches to help in the evaluation, classification, and localization of road distresses in a timely and cost-effective manner.

To facilitate progress in this field, we have developed an open-source UDTIRI benchmark suite. We hope this benchmark suite will enable the use of powerful UDT techniques in IRI, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential.

Copyright

All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.

Citation

@article{mias2024udtiri, title={{UDTIRI}: An Online Open-Source Intelligent Road Inspection Benchmark Suite}, author={Guo, Sicen and Li, Jiahang and Feng, Yi and Zhou, Dacheng and Zhang, Denghuang and Chen, Chen and Su, Shuai and Zhu, Xingyi and Chen, Qijun and Fan, Rui}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2024}, note={{DOI}: 10.1109/TITS.2024.3351209} }