提出时空基础模型框架，统一处理感知、管理与挖掘任务。
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
- 构建覆盖数据全链条的时空基础模型架构
- 突破传统模型任务专一性，提升跨场景泛化能力
- 适合城市计算、气候科学等多领域研究者参考
时空（ST）数据科学涵盖空间与时间维度的大规模数据感知、管理与挖掘，是理解城市计算、气候科学、智能交通等领域复杂系统的基础。传统深度学习方法虽在时空数据挖掘阶段取得显著进展，但普遍任务特定且需大量标注数据。受大语言模型等基础模型成功启发，研究者开始探索时空基础模型（STFM），以提升多样任务间的适应性与泛化能力。与以往架构不同，STFM可赋能时空数据科学全流程，从数据感知、管理到挖掘，提供更全面、可扩展的解决方案。尽管进展迅速，当前对STFM的系统性研究仍不足。本综述旨在全面回顾现有STFM方法，分类总结技术路径，并识别关键研究方向，推动时空通用智能发展。
原文摘要 · Abstract (English)
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation. Traditional deep learning approaches have significantly advanced this field, particularly in the stage of ST data mining. However, these models remain task-specific and often require extensive labeled data. Inspired by the success of Foundation Models (FM), especially large language models, researchers have begun exploring the concept of Spatio-Temporal Foundation Models (STFMs) to enhance adaptability and generalization across diverse ST tasks. Unlike prior architectures, STFMs empower the entire workflow of ST data science, ranging from data sensing, management, to mining, thereby offering a more holistic and scalable approach. Despite rapid progress, a systematic study of STFMs for ST data science remains lacking. This survey aims to provide a comprehensive review of STFMs, categorizing existing methodologies and identifying key research directions to advance ST general intelligence.
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