地理图斑智能计算及模式挖掘方法研究

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。

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

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.