Smart City Governance Based on Multimodal Large-Scale Models

DOI

10.2991/978-94-6463-728-1_83How to use a DOI?

Keywords

multi-modal large-scale models; big data; urban governance

Abstract

In the era of big data, the refined digitization of urban governance has led to the explosive growth of information data generated by various media. Effectively collecting, managing, and utilizing urban governance data is particularly important. The traditional work order generation process of innovative city governance also has problems such as minimal data source types, a heavy workload for manually entering grid event work orders, or the inability of automatically generated workorders to identify long-tail event categories. Through the use of mainstream multi-modal large-scale models, the exploration and optimization of prompt texts, and the construction and fine-tuning of proprietary scene data sets, the event recognition accuracy of the model can be effectively improved. Experimental results show that the multi-modal data analysis, the discovery of long-tail event categories, and intelligent analysis and decision-making achieved using multi-modal large-scale models have good results. Finally, forward-looking thinking and outlook are given on the future development of large-scale models in social governance.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaqing Shen
AU  - Chao Hu
PY  - 2025
DA  - 2025/05/19
TI  - Smart City Governance Based on Multimodal Large-Scale Models
BT  - Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)
PB  - Atlantis Press
SP  - 915
EP  - 924
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-728-1_83
DO  - 10.2991/978-94-6463-728-1_83
ID  - Shen2025
ER  -