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时空异常探测:从数据驱动到知识驱动的内涵转变与实现路径

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  • 万方数据
  • 维普
时空异常探测是地理空间数据挖掘的核心技术之一,可为深层揭示地理过程演化机理提供关键突破口.在大数据与人工智能技术的推动下,从数据驱动向知识驱动转变是地理大数据时空异常智能探测的发展趋势.本文系统梳理了时空异常探测研究的发展历程与主流研究思路,剖析了数据、信息与知识间的辩证关系,并从"地理变量、空间基底、时空关系、知识类型"四位一体的视角构建了时空知识的统一描述框架.进而,结合案例阐述了时空知识与时空异常的"双向驱动"内涵,提出了"时空知识关联建模→时空异常智能识别→时空知识动态更新"的时空异常智能探测实现路径,支撑时空异常可靠探测与时空知识可信服务.
Spatio-temporal anomaly detection:connotation transformation and im-plementation path from data-driven to knowledge-driven modeling
As one of the critical technologies of geo-spatial data mining,spatio-temporal anomaly detection has the capacity of providing key breakthroughs for deeply revealing the evolution mechanism of geographic processes.Promoted by the big data and artificial intelligence technology,the transformation from data-driven to knowledge-driven modeling is the development tendency for the intelligent detection of spatio-temporal anomalies from geographic big data.This paper systematically sorts out the development process and the mainstream study ideas of current spatio-temporal anomaly detection.Through analyzing the dialectical relationships among data,information and knowledge,a unified description framework of spatio-temporal knowledge is constructed by integrating geographic variables,space basis,spatio-temporal relationships and knowledge types.Then,the connotation of bidirectional driving between spatio-temporal knowledge and spatio-temporal anomalies is elaborated with the help of practical cases.The implementation path for intelligent detection of spatio-temporal anomalies is further proposed,which includes spatio-temporal knowledge correlation modeling,spatio-temporal anomaly intelligent detection and spatio-tem-poral anomaly-based knowledge dynamic updating,so as to support both the reliable spatio-temporal anomaly detection and the credible spatio-temporal knowledge services.

spatio-temporal anomalygeographical big dataspatio-temporal knowledgeknowledge graphdeep learning

石岩、王达、邓敏、杨学习

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中南大学地球科学与信息物理学院,湖南长沙 410083

湖南省地理空间信息工程技术研究中心,湖南长沙 410018

江西师范大学地理与环境学院,江西南昌 330022

时空异常 地理大数据 时空知识 知识图谱 深度学习

国家重点研发计划国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金湖南省自然科学基金湖南省科技创新计划中南大学创新驱动计划中南大学前沿交叉研究项目江西省"双千计划"第三批引进类创新领军人才短期项目

2021YFB39009042022YFB39042034207145242371477422714852022JJ200592023RC30322023CXQD0132023QYJC002jxsq2020102062

2024

10.11947/j.AGCS.2024.20230341

测绘学报
中国测绘学会

测绘学报

CSTPCD北大核心
影响因子：1.602
ISSN：1001-1595
年,卷(期)：2024.53(8)