北京大学学报（自然科学版）2024，Vol.60Issue(4)：673-681,9.DOI:10.13209/j.0479-8023.2024.045
大尺度自然地理实体边界智能化提取方法
Intelligent Boundary Extraction Method for Large-scale Physical Geographical Objects: Taking Dabie Mountains as an Example
摘要
Abstract
In order to solve the problem of expressing the distribution range or boundary of physical geographical objects (PGO) in maps with determinate semantics but indeterminate spatial location or distribution range,an intelligent extraction method for PGO's boundary is proposed.Firstly,the given semantic words is used to search big data of the network map.Secondly,the spatial clustering algorithm is used to determine the approximate range of PGO.Finally,considering the geometric characteristics of PGO,such as the undulations of mountains,a feature recognition algorithm is used to further determine the distribution range and boundaries of PGO.Taking into account the complexity of such objects,only the mountain (Dabie Mountains) was taken as an example to proved the effectiveness of the proposed method.关键词
大尺度自然地理实体/地图大数据/智能化提取Key words
large-scale physical geographical objects/map big data/intelligent extraction引用本文复制引用
杨涵珺,孙敏,楼夏寅,杨仕浩..大尺度自然地理实体边界智能化提取方法[J].北京大学学报（自然科学版）,2024,60(4):673-681,9.基金项目
内蒙古自治区科技重大专项(201701B)资助 （201701B）
