UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios

Baichuan Zhou, Haote Yang, Dairong Chen, Junyan Ye, Tianyi Bai, Jinhua Yu, Songyang Zhang, Dahua Lin, Conghui He, Weijia Li

AAAI 2025 pp. 10707-10715

doi:10.1609/AAAI.V39I10.33163 /aaai/2025/zhou2025aaai-urbench/

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Cite

Text

Zhou et al. "UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I10.33163

Markdown

[Zhou et al. "UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/zhou2025aaai-urbench/) doi:10.1609/AAAI.V39I10.33163

BibTeX

@inproceedings{zhou2025aaai-urbench,
  title     = {{UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios}},
  author    = {Zhou, Baichuan and Yang, Haote and Chen, Dairong and Ye, Junyan and Bai, Tianyi and Yu, Jinhua and Zhang, Songyang and Lin, Dahua and He, Conghui and Li, Weijia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {10707-10715},
  doi       = {10.1609/AAAI.V39I10.33163},
  url       = {https://mlanthology.org/aaai/2025/zhou2025aaai-urbench/}
}