Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

2025-03-12

AI Summary

Traditional deep learning models in spatiotemporal data science suffer from strong task specificity and heavy reliance on large-scale labeled datasets. Method: This paper formally introduces and defines the concept of Spatiotemporal Foundation Models (STFMs) for the first time, aiming to establish a general-purpose spatiotemporal intelligence paradigm applicable to urban computing, climate science, and related domains. It proposes a comprehensive STFM methodology taxonomy—integrating self-supervised pretraining, multimodal spatiotemporal representation learning, prompt engineering, and large-model architectural design—and identifies six core research directions. Contribution/Results: This work fills a critical gap by providing the first systematic survey and conceptual framework for STFMs. It advances both theoretical foundations and practical pathways toward spatiotemporal artificial general intelligence, offering essential guidance for future research and development in foundational spatiotemporal modeling.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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.

Problem

• Addressing limitations of task-specific deep learning in spatio-temporal data mining.
• Exploring Spatio-Temporal Foundation Models for enhanced adaptability and generalization.
• Providing a systematic review and research directions for STFMs in data science.

Innovation

• Spatio-Temporal Foundation Models enhance adaptability.
• STFMs integrate sensing, management, and mining.
• STFMs reduce dependency on labeled data.