Multimodal Vision-Language Learning for Urban Transportation Scene Understanding and Air Quality Exposure Assessment
Keywords:
multimodal learning; vision-language models; urban transportation; air quality; exposure assessment; smart cities; socio-technical infrastructureAbstract
Urban transportation systems now generate extensive multimodal data streams that include street-level imagery, natural language descriptions, geospatial records, vehicle trajectory data, and airborne pollutant measurements. Conventional analytical pipelines typically process these modalities separately, which limits their capacity to support integrated environmental health assessment. This paper examines the emerging role of vision-language learning as a unifying computational paradigm for urban transportation scene understanding and air quality exposure assessment. It develops a system-level perspective that connects contrastive and generative multimodal architectures with transportation sensing infrastructure, exposure modeling, and governance. The analysis addresses architectural trade-offs between global semantic alignment and spatially fine-grained scene parsing, the integration of street-view and mixed-view panorama synthesis with sensor calibration, and the use of language interfaces to translate complex visual and environmental evidence into actionable health-relevant information. Rather than proposing a single algorithmic solution, the paper advances a transdisciplinary framing in which multimodal models operate as boundary objects linking computer vision, transportation engineering, environmental epidemiology, and urban policy. It further discusses robustness under distribution shift, fairness across neighborhoods and transit modes, data documentation, deployment feasibility, and long-term sustainability. The central argument is that the value of vision-language systems in this domain depends less on benchmark accuracy than on their capacity to function within heterogeneous urban data infrastructures and to support accountable environmental health decisions.
References
1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
3. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (pp. 8748–8763). PMLR.
4. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations.
5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
6. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.
7. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (pp. 4171–4186).
8. Li, J., Li, D., Xiong, C., & Hoi, S. (2022). BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Proceedings of the 39th International Conference on Machine Learning (pp. 12888–12900). PMLR.
9. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015–4026).
10. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (pp. 405–421). Springer.
11. Xiong, Z., Xing, X., Workman, S., Khanal, S., & Jacobs, N. (2024). Mixed-view panorama synthesis using geospatially guided diffusion. Transactions on Machine Learning Research.
12. Wang, Z., Zhang, X., Wang, L., Fu, S., Wu, J., Xiong, J., & Huang, S. (2024). Concentrations and short-term health effects of VOCs in explored subway, bus and taxi in Beijing, China. Atmospheric Environment, 339, 120855.
13. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851.
14. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684–10695).
15. Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., & Bojanowski, P. (2024). DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research.
16. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research.
17. OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
18. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012–10022).
19. World Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.
20. European Environment Agency. (2023). Air quality in Europe 2023 report. Publications Office of the European Union.
21. Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017). Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE International Conference on Computer Vision (pp. 843–852).
22. Bellinger, C., Mohomed Jabbar, M. S., Zaïane, O., & Osornio-Vargas, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17, Article 907.
23. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
24. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229).
25. Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair. Nature, 559(7714), 324–326.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
