Urban sensing in the era of large language models.

Abstract

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.