雷峰网·2025-12-19 10:29Core Viewpoint - The article discusses the challenges and developments in spatiotemporal AI, emphasizing the need for AI to transition from virtual to physical worlds to unlock its full industrial value [3][4][39]. Group 1: Challenges of Spatiotemporal AI - Spatiotemporal AI faces three main challenges: data scarcity and high collection costs, weak modeling capabilities due to unknown physical laws, and difficulties in creating closed-loop intelligent solutions [4][8][20]. - Data in the physical world is often limited, with high costs and long collection cycles, making it difficult to gather sufficient information for effective modeling [4][9]. - Existing models do not adequately account for spatiotemporal attributes, complicating the application of AI in real-world scenarios [9][20]. Group 2: Development Stages of Spatiotemporal AI - The development of spatiotemporal AI has progressed through five stages, starting from classic models in 1960-1995 to the current focus on city-scale models [26][32][34]. - The second stage (1995-2008) involved discovering spatiotemporal patterns, leading to the application of these patterns in various scenarios, including public health [27][28]. - The third stage (2009-2016) saw the integration of classic machine learning with spatiotemporal features, significantly improving predictive accuracy in air quality monitoring [29][30]. - The fourth stage (2016-2030) introduced deep learning techniques to handle complex spatiotemporal data, particularly in urban environments [32][33]. - The current stage (2023-2035) emphasizes the need for multi-source data fusion and the development of urban intelligence systems, integrating various data types for comprehensive city management [34][35]. Group 3: Application in Smart Cities - The article highlights Xiong'an New Area as a model for smart city development, utilizing spatiotemporal AI to manage urban operations effectively [39][40]. - Real-time data analysis in Xiong'an allows for proactive management of resources, such as electricity and public safety, demonstrating the practical applications of spatiotemporal AI [39][40]. - The integration of various data types, including traffic, weather, and demographic information, is crucial for creating a responsive urban intelligence system [34][39].