地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 57-75.doi: 10.12082/dqxxkx.2020.190462

• 专辑:地理智能 • 上一篇    下一篇

地理图斑智能计算及模式挖掘方法研究

骆剑承1,2, 吴田军3,*(), 吴志峰4, 周亚男5, 郜丽静1,2, 孙营伟1,2, 吴炜6, 杨颖频1,2, 胡晓东1,2, 张新1,2, 沈占锋1,2   

  1. 1. 中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 长安大学 地质工程与测绘学院,西安 710064
    4. 广州大学地理科学学院,广州 510006
    5. 河海大学地球科学与工程学院,南京 211100
    6. 浙江工业大学计算机学院,杭州 310023
  • 收稿日期:2019-08-21 修回日期:2019-11-04 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 吴田军 E-mail:wutianjun1986@163.com
  • 作者简介:骆剑承(1970— ),男,浙江杭州人,博士,研究员,主要从事遥感大数据智能计算研究。E-mail:luojc@radi.ac.cn
  • 基金资助:
    国家自然科学基金项目(41631179);国家自然科学基金项目(41601437);国家重点研发计划项目(2017YFB0503600)

Methods of Intelligent Computation and Pattern Mining based on Geo-parcels

LUO Jiancheng1,2, WU Tianjun3,*(), WU Zhifeng4, ZHOU Ya'nan5, GAO Lijing1,2, SUN Yingwei1,2, WU Wei6, YANG Yingpin1,2, HU Xiaodong1,2, ZHANG Xin1,2, SHEN Zhanfeng1,2   

  1. 1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710064, China
    4. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
    5. School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
    6. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • Received:2019-08-21 Revised:2019-11-04 Online:2020-01-25 Published:2020-04-08
  • Contact: WU Tianjun E-mail:wutianjun1986@163.com
  • Supported by:
    National Natural Science Foundation of China(41631179);National Natural Science Foundation of China(41601437);National Key Research and Development Program of China(2017YFB0503600)

摘要:

在大数据时代,高分辨率对地观测技术实现了对地球表层地理现象和地理过程最为真实、量化、全面覆盖又快速更新的数据化记录,可为地理空间认知研究的新发展奠定时空信息聚合与挖掘计算的基准。地理图斑是影像空间映射到地理空间中对于地理实体的抽象化表达,是构建地理场景和承载地理空间各类信息进而开展模式挖掘的最小单元。本文以地理图斑为基本对象,通过分析其中视觉模拟、符号推测等几类机器学习的协同计算机制,从空间、时间与属性等维度构建了集“分区分层感知”、“时空协同反演”、“多粒度决策”三者于一体的地理图斑智能计算模型,并以在贵州息烽县、广西江州区开展的农业种植结构制图与规划决策为应用案例,探索了地理图斑分布、生长以及功能3种模式的挖掘方法,并进一步设计了动态视角下开展图斑动力模式挖掘的研究思路。

关键词: 地理图斑, 分区分层感知, 时空协同反演, 多粒度决策, 分布模式, 生长模式, 功能模式, 动力模式

Abstract:

In the era of big data, high-resolution Earth observation technologies have been able to provide the most authentic, quantitative, comprehensive-coverage, and fast-updating data about the geographic phenomena and processes on the Earth's surface. Such data provide precise spatiotemporal benchmarks of information aggregation and computation of data mining for new developments of geospatial cognitive research. Geo-parcels are abstract expressions for mapping geographical entities from image-space to geographic-space. Geo-parcels are the smallest units of pattern mining with the construction of geographic scenes and loading various geospatial information. In this paper, a synergistic calculation mechanism with the machine learning methods of visual simulation and symbol inference were analyzed based on the basic unit of geo-parcels. From the dimensions of space, time, and attribute, we constructed an intelligent computation model based on geo-parcels by integrating three sub-models: zoning-stratified perception, spatiotemporal synergistically inversion, and multi-granular decision-making. Furthermore, this paper explored the pattern mining methods of geo-parcels for their distribution, growth, and function via two case studies: the agricultural planting structure mapping in Xifeng County, Guizhou province and the planning decision in Jiangzhou District of Guangxi Zhuang Autonomous Region.

Key words: geo-parcel, zoning-stratified perception, spatiotemporal synergistically inversion, multi-granular decision-making, distribution pattern, growth pattern, functional pattern, dynamic pattern