Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities

• URL: http://arxiv.org/abs/2501.09045v2
• Date: Fri, 07 Feb 2025 02:39:32 GMT
• Title: Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
• Authors: Adam Goodge, Wee Siong Ng, Bryan Hooi, See Kiong Ng,
• Abstract summary: Foundation models (STFMs) have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and generalization capabilities necessary for broad applicability. We explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.
• Score: 48.45951497996322
• License:
• Abstract: 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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