Machine learning techniques are now integral to the advancement of
intelligent urban services, playing a crucial role in elevating the efficiency,
sustainability, and livability of urban environments. The recent emergence of
foundation models such as ChatGPT marks a revolutionary shift in the fields of
machine learning and artificial intelligence. Their unparalleled capabilities
in contextual understanding, problem solving, and adaptability across a wide
range of tasks suggest that integrating these models into urban domains could
have a transformative impact on the development of smart cities. Despite
growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces
challenges such as a lack of clear definitions, systematic reviews, and
universalizable solutions. To this end, this paper first introduces the concept
of UFM and discusses the unique challenges involved in building them. We then
propose a data-centric taxonomy that categorizes current UFM-related works,
based on urban data modalities and types. Furthermore, to foster advancement in
this field, we present a promising framework aimed at the prospective
realization of UFMs, designed to overcome the identified challenges.
Additionally, we explore the application landscape of UFMs, detailing their
potential impact in various urban contexts. Relevant papers and open-source
resources have been collated and are continuously updated at
this https URL

机器学习在智能城市服务的发展中起着至关重要的作用，最近出现的基础模型，如 ChatGPT，在机器学习和人工智能领域标志着一个革命性的转变。本文首先介绍了城市基础模型的概念，并讨论了构建它们所面临的独特挑战。然后，我们提出了一个以数据为中心的分类法，将当前的城市基础模型相关工作按照城市数据的模态和类型进行分类。此外，为了促进这一领域的发展，我们提出了一个有前景的框架，旨在解决已确定的挑战。此外，我们探讨了城市基础模型的应用领域，详细说明了它们在各种城市背景下的潜在影响。