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

2024-01-30

arXiv.org

AI Summary

Current Urban Foundation Models (UFMs) lack formal definitions, systematic taxonomies, and a unifying framework, hindering the advancement of Urban General Intelligence (UGI). Method: This work formally defines UFMs for the first time and establishes the first taxonomy driven by multimodal urban data. It proposes a general UGI-oriented framework supporting cross-modal understanding, cross-task transfer, and cross-regional co-evolution—integrating data-center paradigms, abstract foundation model architectures, cross-domain alignment techniques, and open-source collaborative governance mechanisms. Contributions/Results: We introduce a unified conceptual paradigm and research landscape; launch Awesome-Urban-Foundation-Models—a continuously updated, authoritative resource repository; and deliver reusable methodologies and technical roadmaps. Collectively, these advances enable a paradigm shift in smart cities—from narrow, task-specific AI toward generalized, collaborative, and self-evolving urban intelligence.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Intelligent User Interfaces

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 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.

Problem

• Urban Foundation Models
• Universal Urban Intelligence
• Smart City Construction

Innovation

• Urban Foundation Models
• Classification Method
• Universal General Intelligence