测绘学报 ›› 2024, Vol. 53 ›› Issue (4): 736-749.doi: 10.11947/j.AGCS.2024.20230316
收稿日期:
2023-08-07
修回日期:
2024-02-04
发布日期:
2024-05-13
通讯作者:
鲁谢春
E-mail:chenzl@cug.edu.cn;xiechunlu@cug.edu.cn
作者简介:
陈占龙(1980—),男,博士,教授,研究方向为空间分析算法、空间推理、地理信息系统软件开发与应用。E-mail:chenzl@cug.edu.cn
基金资助:
Zhanlong CHEN1,2,3(), Xiechun LU1(), Yongyang XU2,3
Received:
2023-08-07
Revised:
2024-02-04
Published:
2024-05-13
Contact:
Xiechun LU
E-mail:chenzl@cug.edu.cn;xiechunlu@cug.edu.cn
About author:
CHEN Zhanlong (1980—), male, PhD, professor, majors in spatial analysis algorithms, spatial reasoning, geographic information system software and application development. E-mail: chenzl@cug.edu.cn
Supported by:
摘要:
建筑物要素合并是大比例尺地图缩编过程中实现空间结构简化的重要手段。基于综合规则的合并方法难以同时顾及要素形态、分布等诸多特征,受预设算法参数影响大,综合过程缺乏灵活性。针对这一问题,本文提出了一种基于图顶点深度聚类网络的建筑物合并模型,利用Delaunay三角网构建建筑物群组表征图模型,结合自编码器与图卷积网络学习剖分三角形的几何形态、空间分布特征,采用自监督学习方式实现三角形的聚类与分类(保留、删除),最终在不依赖样本条件下实现建筑物要素端到端智能化合并。试验表明,该方法对预设合并参数依赖低,能同时顾及建筑物要素的形态与分布特征。合并过程具有一定灵活性,合并结果能较好满足地图可视化要求。
中图分类号:
陈占龙, 鲁谢春, 徐永洋. 基于图顶点深度聚类的建筑物合并方法[J]. 测绘学报, 2024, 53(4): 736-749.
Zhanlong CHEN, Xiechun LU, Yongyang XU. A building aggregation method based on deep clustering of graph vertices[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 736-749.
表1
三角形形状参量计算"
参量名称 | 计算方法 | 示意图 |
---|---|---|
面积Area(v) | 描述三角形大小的参量,通常使用公式Area(v)=1/2a×h计算 | |
最小外接矩形方向Oritation(v) | 三角形最小接矩形在二维平面中的朝向,此处计算为朝向与x轴的夹角,Oritation(v)∈[0,1] | |
三角形底与高的比值Ratio(v) | 三角形的形状可以由三角形底与高的比值,以及底边对角的角度唯一确定[ | |
三角形底边对角的角度Angle(v) | 三角形最长边对角的角度(此处计算采用最长边作为底边),Angle(v)∈(0,π) | |
多比例尺综合约束距离Tolerant_Dis(v,δ) | 描述三角形形态与综合约束距离δ之间的参量。当三角形一边与建筑物原始轮廓重合时,记该边到对角的距离为dis,则Tolerant_Dis(v,δ)=e-max[(dis-δ),0];若三角形有多条边与建筑物轮廓重合,则三边高均小于δ(点加密距离小于等于δ情况下),因此Tolerant_Dis(v,δ)=1;若三角形每一条边均不与建筑物轮廓重合,则dis为三边高的均值 | |
三角形之间的距离Distance(u,v) | 两三角形重心间的欧氏距离。(x1,y1)、(x2,y2)分别为两三角形重心坐标,Distan (u, |
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