[论文解读] Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

Weijia Zhang, Jindong Han+5 more|arXiv (Cornell University)|Jan 30, 2024

Human Mobility and Location-Based Analysis被引用 6

一句话总结

本文定义了城市基础模型（UFMs），提供以数据为中心的分类法，按数据模态对当前城市基础模型进行综述，并提出实现城市通用智能（UGI）的框架及未来方向。

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 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.

研究动机与目标

• 定义城市基础模型（UFMs），并阐明它们在推进城市通用智能（UGI）中的作用。
• 提供基于数据的城市数据模态分类法，以组织现有工作。
• 识别构建UFMs的核心挑战，并勾勒面向多场景城市应用的通用UFMs的前瞻性框架。

提出的方法

• 给出UFMs的正式定义和基本概念。
• 提出基于数据的分类法，将UFMs按城市数据模态进行分类（语言、视觉、时间序列、轨迹、地理向量、多模态、其他）。
• 回顾预训练与适应技术（监督、生成、对比、混合；微调、提示调优、提示工程）。
• 总结当前UFMs的基准和数据集，并讨论基于地理向量的UFMs。
• 提出一个前瞻性框架，用于构建可跨越多种城市任务和领域的多功能UFMs。

实验结果

研究问题

• RQ1 什么是UFMs，它们与城市通用智能（UGI）有何关系？
• RQ2 如何按城市数据模态和发展技术对UFMs进行系统分类？
• RQ3 阻碍UFMs的主要挑战有哪些？哪些框架能够支持面向城市应用的多功能、可泛化的UFMs？

主要发现

• UFMs在多来源、多粒度、以及多模态的城市数据上进行预训练，能够为多样化的城市领域提供通用能力。
• 以数据为中心的分类法将UFMs分为七种数据模态：语言、视觉、时间序列、轨迹、地理向量、多模态及其他。
• UFMs依赖于预训练（监督、生成、对比、混合）和适应（微调、提示调优、提示工程）以实现任务的多样性。
• 主要挑战包括跨源数据整合、时空推理、跨域通用性，以及隐私/安全等问题。
• 提出一项前瞻性框架，以指导开发能够在城市任务和领域间实现泛化的多功能UFMs。