The integration of machine learning techniques has become a cornerstone in the development of intelligent urban services, significantly contributing to the enhancement of urban efficiency, sustainability, and overall livability. Recent advancements in foundational models, such as ChatGPT, have introduced a paradigm shift within the fields of machine learning and artificial intelligence. These models, with their exceptional capacity for contextual comprehension, problem-solving, and task adaptability, present a transformative opportunity to reshape the future of smart cities and drive progress toward Urban General Intelligence (UGI). Despite increasing attention to Urban Foundation Models (UFMs), this rapidly evolving field faces critical challenges, including the lack of clear definitions, systematic reviews, and universalizable solutions. To address these issues, this paper first introduces the definition and concept of UFMs and highlights the distinctive challenges involved in their development. Furthermore, we present a data-centric taxonomy that classifies existing research on UFMs according to the various urban data modalities and types. In addition, we propose a prospective framework designed to facilitate the realization of versatile UFMs, aimed at overcoming the identified challenges and driving further progress in this field. Finally, this paper systematically summarizes and discusses existing benchmarks and datasets related to UFMs, and explores the wide-ranging applications of UFMs within urban contexts, illustrating their potential to significantly impact and transform urban systems. A comprehensive collection of relevant research papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.

翻译：机器学习技术的整合已成为发展智能城市服务的基石，显著提升了城市效率、可持续性和整体宜居性。近期基础模型（如ChatGPT）的突破，为机器学习和人工智能领域带来了范式转变。这类模型凭借其卓越的上下文理解、问题解决与任务适应能力，为重塑智慧城市的未来、推动实现城市通用智能（UGI）提供了变革性机遇。尽管城市基础模型（UFMs）日益受到关注，但这一快速发展领域仍面临关键挑战，包括缺乏明确定义、系统综述及通用解决方案。针对这些问题，本文首先介绍了UFMs的定义与概念，并着重阐述了其开发过程中的独特挑战。其次，我们提出了一种以数据为中心的分类方法，根据不同的城市数据模态与类型对现有UFM研究进行分类。此外，我们设计了一个前瞻性框架，旨在促进多功能UFMs的实现，以克服上述挑战并推动该领域的进一步发展。最后，本文系统总结与讨论了现有UFM相关基准与数据集，并探索了UFM在城市场景中的广泛应用，展示了其显著影响与变革城市系统的潜力。相关研究论文及开源资源已汇编整理，并持续更新于：https://github.com/usail-hkust/Awesome-Urban-Foundation-Models。