Hou Ce, Zhang Fan, Li Yong, Li Haifeng, Mai Gengchen, Kang Yuhao, Yao Ling, Yu Wenhao, Yao Yao, Gao Song, Chen Min, Liu Yu
Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China.
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
Innovation (Camb). 2025 Jan 6;6(1):100749. doi: 10.1016/j.xinn.2024.100749.
Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs, particularly in the potential of advancing next-generation urban sensing for exploring urban mechanisms. The discussion navigates through several key aspects, including enhancing knowledge transfer between humans and LLM, urban mechanisms awareness, and achieve automated decision-making with LLM agents. We emphasize the potential of LLMs to revolutionize urban sensing, offering a more comprehensive, efficient, and in-depth understanding of urban dynamics, and also acknowledge challenges in multi-modal data utilization, spatial-temporal cognition, cultural adaptability, and privacy preservation. The future of urban sensing with LLMs lies in leveraging their emerged intelligent and addressing these challenges to achieve more intelligent, responsible, and sustainable urban development.
随着城市发展成为人类活动的中心，城市感知变得越来越重要。大语言模型（LLMs）基于通过其以语言为中心的框架而形成的常识和世界观，为城市感知提供了新的机遇。本文阐述了大语言模型的变革性影响，特别是在推进下一代城市感知以探索城市机制方面的潜力。讨论涉及几个关键方面，包括增强人与大语言模型之间的知识转移、城市机制认知，以及利用大语言模型智能体实现自动化决策。我们强调大语言模型有潜力彻底改变城市感知，提供对城市动态更全面、高效和深入的理解，同时也认识到在多模态数据利用、时空认知、文化适应性和隐私保护方面存在的挑战。利用大语言模型进行城市感知的未来在于发挥其新出现的智能并应对这些挑战，以实现更智能、负责任和可持续的城市发展。
