Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-makin...Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-making processes.As artificial intelligence advances,intelligent spatial-temporal algorithms have found extensive applications across various disciplines,such as geosciences,biology,and public health.1 Compared to traditional methods,these algorithms are data driven,making them well suited for addressing the complexities of modeling real-world systems.However,their reliance on substantial domain-specific expertise limits their broader applicability.Recently,significant advancements have been made in spatial-temporal large models.Trained on large-scale data,these models exhibit a vast parameter scale,superior generalization capabilities,and multitasking advantages over previous methods.Their high versatility and scalability position them as promising super hubs for multidisciplinary research,integrating knowledge,intelligent algorithms,and research communities from different fields.Nevertheless,achieving this vision will require overcoming numerous critical challenges,offering an expansive and profound space for future exploration.展开更多
Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the info...Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the information capture capabilities of single-modality sensors.Multimodal perception addresses these by integrating complementary multisource heterogeneous information,providing a solution for perceiving complex environments.This technology spans across fields such as autonomous driving,industrial detection,biomedical engineering,and remote sensing.However,challenges arise due to multisensor misalignment,inadequate appearance forms,and perception-oriented issues,which complicate the corresponding relationship,information representation,and task-driven fusion.In this context,the advancement of artificial intelligence(AI)has driven the development of information fusion,offering a new perspective on tackling these challenges.1 AI leverages deep neural networks(DNNs)with gradient descent optimization to learn statistical regularities from multimodal data.By examining the entire process of multimodal information fusion,we can gain deeper insights into AI’s working mechanisms and enhance our understanding of AI perception in complex environments.展开更多
基金supported by NSFC No.62372430the Youth Innovation Promotion As-sociation CAS No.2023112.
文摘Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-making processes.As artificial intelligence advances,intelligent spatial-temporal algorithms have found extensive applications across various disciplines,such as geosciences,biology,and public health.1 Compared to traditional methods,these algorithms are data driven,making them well suited for addressing the complexities of modeling real-world systems.However,their reliance on substantial domain-specific expertise limits their broader applicability.Recently,significant advancements have been made in spatial-temporal large models.Trained on large-scale data,these models exhibit a vast parameter scale,superior generalization capabilities,and multitasking advantages over previous methods.Their high versatility and scalability position them as promising super hubs for multidisciplinary research,integrating knowledge,intelligent algorithms,and research communities from different fields.Nevertheless,achieving this vision will require overcoming numerous critical challenges,offering an expansive and profound space for future exploration.
文摘Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the information capture capabilities of single-modality sensors.Multimodal perception addresses these by integrating complementary multisource heterogeneous information,providing a solution for perceiving complex environments.This technology spans across fields such as autonomous driving,industrial detection,biomedical engineering,and remote sensing.However,challenges arise due to multisensor misalignment,inadequate appearance forms,and perception-oriented issues,which complicate the corresponding relationship,information representation,and task-driven fusion.In this context,the advancement of artificial intelligence(AI)has driven the development of information fusion,offering a new perspective on tackling these challenges.1 AI leverages deep neural networks(DNNs)with gradient descent optimization to learn statistical regularities from multimodal data.By examining the entire process of multimodal information fusion,we can gain deeper insights into AI’s working mechanisms and enhance our understanding of AI perception in complex environments.
