TY - GEN
T1 - Foundation Models for Spatio-Temporal Data Science
T2 - 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
AU - Liang, Yuxuan
AU - Wen, Haomin
AU - Xia, Yutong
AU - Jin, Ming
AU - Yang, Bin
AU - Salim, Flora
AU - Wen, Qingsong
AU - Pan, Shirui
AU - Cong, Gao
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2025/8/3
Y1 - 2025/8/3
N2 - 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.
AB - 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.
KW - LLM
KW - Spatio-temporal data
KW - foundation model
KW - urban computing
UR - https://www.scopus.com/pages/publications/105014313642
U2 - 10.1145/3711896.3736552
DO - 10.1145/3711896.3736552
M3 - 会议稿件
AN - SCOPUS:105014313642
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 6063
EP - 6073
BT - KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 3 August 2025 through 7 August 2025
ER -
