Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
This is an incremental review paper that addresses the problem of underdeveloped STFMs for researchers and practitioners in fields like transportation and environmental monitoring.
The paper tackles the lack of success in spatio-temporal foundation models (STFMs) compared to other AI domains, by outlining a vision, assessing current gaps, and identifying challenges and opportunities for advancement.
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.