构建通用时空模型，推动交通与环境等领域的智能分析
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
- 提出时空基础模型的愿景与核心能力要求
- 指出现有研究在泛化性与实用性上的关键差距
- 为跨领域应用提供可拓展的研究方向
基础模型已彻底改变人工智能，大幅提升了视觉与语言任务的性能，并催生了多项变革性能力。然而，在交通、公共卫生与环境监测等关键领域中广泛存在的时空数据，其对应的时空基础模型（STFMs）尚未取得同等进展。本文阐述了未来STFMs的发展愿景，明确了其必要特征与泛化能力，系统评估了当前研究现状，揭示了与理想目标之间的差距，并指出阻碍其发展的关键挑战。最后，探讨了潜在机遇与前进方向，旨在推动研究迈向高效且广泛应用的时空基础模型。
原文摘要 · Abstract (English)
Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. However, despite the prevalence of spatio-temporal data in critical domains such as transportation, public health, and environmental monitoring, spatio-temporal foundation models (STFMs) have not yet achieved comparable success. In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and the generalization capabilities necessary for broad applicability. We critically assess the current state of research, identifying gaps relative to these ideal traits, and highlight key challenges that impede their progress. Finally, we explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.
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