Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

• URL: http://arxiv.org/abs/2402.01749v1
• Date: Tue, 30 Jan 2024 04:48:16 GMT
• Title: Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
• Authors: Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao Liu, Hui Xiong
• Abstract summary: Recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Despite growing interest in Urban Foundation Models, this burgeoning field faces challenges such as a lack of clear definitions, systematic reviews, and universalizable solutions. We propose a data-centric taxonomy that categorizes current UFM-related works, based on urban data modalities and types.
• Score: 26.517572366783384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.

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