摘要
针对目前人工判定碎片化地形矢量数据比例尺效率低下、可靠性不高的问题,本文提出一种基于自然法则的地形矢量数据比例尺评估方法,实现地形矢量数据比例尺判定的自动化。该方法基于不同比例尺地形矢量数据节点密度指数随比例尺变化的特征,利用自然法则原理和统计学知识确定不同比例尺、不同类型要素节点密度指数区间的理论值;对于待鉴定地形矢量数据,将其节点密度指数与已知比例尺的地形矢量数据节点密度指数区间进行比对,如果落在该比例尺节点密度指数区间内,则推断为该比例尺数据,进而实现比例尺的自动判定;最后利用等高线和水系两种地形要素验证了该评估方法的可行性。试验结果表明,基于本文方法的1∶100万、1∶25万和1∶5万地形矢量数据比例尺自动判定正确率分别达到了93.97%、94.04%和92.47%,地形矢量数据比例尺判定总体正确率达到93.21%,极大提高了比例尺判定的精度与效率,为地理信息安全评估提供了技术支撑。
To address the issues of low efficiency and low reliability in manually determining the scale of terrain vector data,a terrain vector data scale evaluation method based on natural laws is proposed to achieve automation in terrain vector data scale determination in this paper.Based on the characteristics that the node density of terrain vector data at different scales changes with the scale,this method uses the principles of natural law and statistical knowledge to determine the theoretical values of node density intervals at different scales and different elements.For the terrain vector data to be identified,compare its node density with the node density interval of the known scale vector data.If it falls within the node density interval of the scale,it is inferred as the scale data,and then automatic scale determination is achieved.Finally,the feasibility of the evaluation method was verified using two elements:contour lines and water systems.The experimental results show that the accuracy of automatic scale determination of 1∶1000000,1∶250000 and 1∶50000 terrain vector data based on the algorithm proposed in this paper reaches 93.97%,94.04%and 92.47%,respectively,and the overall accuracy of scale determination of vector data reaches 93.21%,greatly improving the accuracy and efficiency of scale judgment,and providing technical support for geographic information security assurance.
作者
刘万增
王新鹏
赵婷婷
翟曦
李然
朱秀丽
蒋志浩
彭云璐
张晔
LIU Wanzeng;WANG Xinpeng;ZHAO Tingting;ZHAI Xi;LI Ran;ZHU Xiuli;JIANG Zhihao;PENG Yunlu;ZHANG Ye(Hubei Luojia Laboratory,Wuhan 430079,China;National Geomatics Center of China,Beijing 100830,China;Key Laboratory of Spatio-temporal Information and Intelligent Services(LSIIS),MNR,Beijing 100830,China)
出处
《测绘学报》
EI
CSCD
北大核心
2024年第6期1013-1024,共12页
Acta Geodaetica et Cartographica Sinica
基金
湖北珞珈实验室开放基金(220100037)
国家自然科学基金(42394062)。