摘要
在土地确权工作中,界址点及界址线的提取是一项重要工作。传统的界址点及界址线提取方法效率很低,且无法应对海量界址点和界址线的提取。针对上述问题,提出并实现了一种基于Model Builder和ArcEngine组件技术的界址点及界址线快速智能提取方法。该方法首先通过Model Builder生成所需的界址点和界址线的地理实体模板文件,再通过ArcEngine读取宗地和模板文件的空间位置及属性信息,从而实现界址点和界址线的快速智能提取。将该方法应用于某地土地确权工作实践,针对实验区域内31 446个宗地图斑进行处理,生成192 263个界址点和221 838条界址线所需的时间小于12分钟。研究表明,所倡导方法能够快速准确地实现对界址点及界址线的智能化提取,极大地节约工作成本、提高工作效率,为我国土地确权及同类工作提供了原创性技术支持。
In the land ownership work,extraction of boundary points and boundary lines is an important work.The traditional method for boundary point and boundary line extraction has very low extraction efficiency,and is unable to cope with the mass boundary points and boundary lines.In response to the above problems,this paper presents a new method for extracting boundary points and boundary lines fast and intelligently by way of the Model Builder and the ArcEngine component technology.Firstly,through the Model Builder to generate the required boundary points and boundary lines of the geographical entity template file,and then,read the block map spot and template files through the ArcEngine spatial location and attribute information,so as to realize the intelligent rapid extraction of boundary points and boundary lines.It applies this method to the area of land ownership in practice.To process the 31 448 figure spots in the test area,we generate 192 263 boundary points and 221 838 boundary lines in less than 12 minutes.Research shows that with this method we can quickly and accurately achieve intelligence on the boundary point and boundary line extraction,greatly saving cost and improving work efficiency.This research can provide original technical support for the land ownership work and similar work in china.
作者
董沈峰
李朝奎
吴柏燕
廖孟光
周访宾
DONG Shenfeng;LI Chaokui;WU Baiyan;LIAO Mengguang;ZHOU Fangbin(National-local Joint Engineering Laboratory of Geo-Spatial Information Technology,Hunan University of Science and Technology,Xiangtan,Hunan 411201,China;School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410000,China)
出处
《遥感信息》
CSCD
北大核心
2018年第5期117-122,共6页
Remote Sensing Information
基金
国家自然科学基金(41571374
41671446)
湖南省教育厅重点项目(16A070)
湖南省自然科学基金湘潭联合基金(2017JJ4037)
特殊环境道路工程湖南省重点实验室开放基金(KFJ150502)
