摘要
海洋是高质量发展的要地,海洋科学大数据的发展为认知和经略海洋带来机遇的同时也引入了新的挑战。海洋科学大数据具有超多模态的显著特征,目前尚未形成面向海洋领域特色的多模态智能计算理论体系和技术框架。因此,本文首次从多模态数据技术的视角,系统性介绍面向海洋现象/过程的智能感知、认知和预知的交叉研究进展。首先,通过梳理海洋科学大数据全生命周期的阶段演进过程,明确海洋多模态智能计算的研究对象、科学问题和典型应用场景。其次,在海洋多模态大数据内容分析、推理预测和高性能计算3个典型应用场景中展开现有工作的系统性梳理和介绍。最后,针对海洋数据分布和计算模式的差异性,提出海洋多模态大数据表征建模、跨模态关联、推理预测以及高性能计算4个关键科学问题中的挑战,并提出未来展望。
The marine-oriented research is essential to high-quality of human-based development.But,the current recognition of the ocean system is less than 5%.To understand the ocean,big marine data is acquired from observation,monitoring,investigation and statistics.Thanks to the development of the multi-scaled ocean observation system,the extensive of multi-modal marine oriented data has developed via remote sensing image,spatio-temporal analysis,simulation data,literature review and video&audio monitoring.To resilient the sustainable development of human society,current deep analysis and multimodal ocean data mining method has promoted the marine understanding on the aspects of ocean dynamic processes,energy and material cycles,the evolution of blue life,scientific discovery,healthy environment,and the quick response of extreme weather and climate change.Compared to traditional big data,the multi-modal big ocean data has its unique features,such as the super-giant system(covering 71%of the earth’s surface,daily increment(10 TB),super multi-perspectives(“land-sea-air-ice-earth based”coupling,“hydrometeorological-acoustical-optical and electromagnetic-based”polymorphism),super spatial scale(“centimeter to hundreds kilometer based”),and temporal scale(“micro-second to inter-decadal based”).These features-derived challenges of existing multi-modal intelligent computing technology have to deal with such problems as cross-scale and multi-modal fusion analyses,multi-disciplinary and multi-domain coordinated reasoning,large computing power based multi-architecture compatible applications.We systematically introduce the cross-cutting researches of intelligent perception,cognition,and prediction for marine phenomena/processes based on multimodal data technology.First,we clarify the research objects,scientific problems,and typical application scenarios of marine multimodal intelligent computing through the evolution analysis of the lifecycle of marine science big data.Next,we target the differences between ocean
作者
聂婕
左子杰
黄磊
王志刚
孙正雅
仲国强
王鑫
王玉成
刘安安
张弘
董军宇
魏志强
Nie Jie;Zuo Zijie;Huang Lei;Wang Zhigang;Sun Zhengya;Zhong Guoqiang;Wang Xin;Wang Yucheng;Liu An'an;Zhang Hong;Dong Junyu;Wei Zhiqiang(Ocean University of China,Qingdao 266100,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266061,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;School of Astronautics,Beihang University,Beijing 100083,China)
出处
《中国图象图形学报》
CSCD
北大核心
2022年第9期2589-2610,共22页
Journal of Image and Graphics
基金
国家重点研发计划资助(2021YFF0704000)
国家自然科学基金项目(62072418,62172376,61872326)
中央高校基本科研业务费专项资金资助(202042008)。
关键词
海洋大数据
多模态
海洋多媒体内容分析
海洋知识图谱
海洋大数据预测
海洋高性能计算
海洋目标重识别
marine big data
multimodal
marine multimedia content analysis
marine knowledge graph
marine big data prediction
marine oriented high performance computing
re-identification of marine object
