复杂建筑物最短邻近线多边形聚合法

doi:10.11947/j.AGCS.2021.20200493

摘要/Abstract

摘要： 建筑物多边形聚合一般是以相离的空间拓扑关系为基础，但是随着数据来源的多源化，复杂建筑物内部多边形的空间拓扑关系多样化甚至不一致，导致难以对此类复杂建筑物进行聚合，而智慧城市中建筑物多尺度可视化要求快速准确地提取复杂建筑物轮廓形状。为此，本文提出一种基于加密顶点间最短邻近线的建筑物多边形聚合方法。首先，基于聚合距离递归搜索邻近多边形以确定聚类范围，便于对地图数据分区处理；然后，对每个聚类区域，按照多边形边线的坐标点顺序，采用线性插值方法为多边形的每条边线生成加密点；在加密多边形边线顶点后，通过网格索引搜索和构建各点与邻近建筑物多边形的最短邻近线，并删除3类异常情况，形成有效最短邻近线集合；随后，按照一定顺序合并有效最短邻近线，形成最短邻近线组，并进一步过滤掉3类特殊情况，确定建筑物多边形的聚合区域；最后，顺时针按最小旋转角原则进行递归搜索，提取包含内环和外环的建筑物多边形外围轮廓。试验证明，该方法能快速探测建筑物多边形之间的邻近聚合区域，实时准确地提取复杂建筑物实体的外轮廓线，处理结果的整体轮廓接近真实情况，为复杂建筑物可视化提供条件。

关键词: 复杂建筑物, 聚合, 拓扑关系, 邻近线

Abstract: The aggregation of buildings is generally based on the disjoint in spatial topological relationships. However, with the diversification of data sources, the spatial topological relationships between polygons inside large buildings also become diversified and even inconsistent, making it difficult to aggregate building polygons, while the multi-scale visualization of buildings in the smart city requires rapid and accurate extraction of the contour shapes of complex buildings. For this reason, this paper proposes an aggregation method of buildings based on the shortest adjacent line between vertexes interpolated. First, recursively search for adjacent polygons based on the aggregation distance to determine the clustering regions, which is convenient for processing the map data in every region. Then, for each clustered region, encrypt vertices for each polygon using a linear interpolation method according to the order of the points on it; after that, search and construct the shortest adjacent line between each vertice and the adjacent polygon through the grid index, and delete the three types of shortest adjacent lines which are abnormal; then, merge the effective shortest adjacent lines in a certain order to form the shortest adjacent line group, and further filter out three kinds of special cases, so as to determine the aggregation area of adjacent polygons; finally, the recursive search is performed clockwise according to the minimum rotation angle, and the contour of polygons including the inner ring and the outer ring is extracted. Experiments show that this method can quickly detect the aggregation area between the adjacent polygons. Experiments in this paper show that this method can quickly detect the adjacent aggregation areas between building polygons and accurately extract the outer contour lines of complex building entities in real-time. The overall contours extracted are close to the actual situation, which provides conditions for building visualization.

Key words: complex buildings, aggregation, topological relationship, adjacent line

中图分类号:

P208

王勇, 罗安, 王慧慧, 曹元晖, 刘纪平. 复杂建筑物最短邻近线多边形聚合法[J]. 测绘学报, 2021, 50(12): 1671-1682.

WANG Yong, LUO An, WANG Huihui, CAO Yuanhui, LIU Jiping. A method of polygon aggregation for complex buildings based on shortest adjacent lines[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12): 1671-1682.

参考文献

[1] 刘先林.为社会进步服务的测绘高新技术[J].测绘科学, 2019, 44(6):1-15. LIU Xianlin. High technologies of surveying and mapping for social progress[J]. Science of Surveying and Mapping, 2019, 44(6):1-15.
[2] 李德仁,肖雄武,郭丙轩,等.倾斜影像自动空三及其在城市真三维模型重建中的应用[J].武汉大学学报(信息科学版), 2016, 41(6):711-721. LI Deren, XIAO Xiongwu, GUO Bingxuan, et al. Oblique image based automatic Aerotriangulation and its application in 3D city model reconstruction[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6):711-721.
[3] GUERCKE R, GÖTZELMANN T, BRENNER C, et al. Aggregation of LoD 1 building models as an optimization problem[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(2):209-222.
[4] KOLBE T H, GRÖGER G, PLÜMER L. CityGML:interoperable access to 3D city models[M]//VAN OOSTEROM P, ZLATANOVA S, FENDEL E M. Geo-information for Disaster Management. Berlin:Springer, 2005:883-899.
[5] 杜福光.基于ArcScene城市三维可视化研究与应用[D].西安:西安科技大学, 2010. DU Fuguang. Research and application of urban three-dimensional visualization based on ArcScene[D]. Xi'an:Xi'an University of Science and Technology, 2010.
[6] 郭仁忠,艾廷华.制图综合中建筑物多边形的合并与化简[J].武汉测绘科技大学学报, 2000, 25(1):25-30. GUO Renzhong, AI Tinghua. Simplification and aggregation of building polygon in automatic map generalization[J].Journal of Wuhan Technical University of Surveying and Mapping, 2000, 25(1):25-30.
[7] DULGHERU F V. Algorithms for map generalization with ArcGIS software[C]//Proceedings of 2011 International Conference of Scientific Paper Afases. Brasov, Romania:[s.n.], 2011:596-599.
[8] 郭建忠,谢明霞,李柱林.基于线缓冲区分析的街区合并方法[J].地理与地理信息科学, 2011, 27(6):111-112. GUO Jianzhong, XIE Mingxia, LI Zhulin. Block aggregation based on line buffer analysis[J]. Geography and Geo-Information Science, 2011, 27(6):111-112.
[9] AI Tinghua, YIN Hongmei, SHEN Yilang, et al. A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation[J]. PLoS One, 2019, 14(7):2188-2207.
[10] 黄诠,刘浩,梁平元. Delaunay三角网的并行构网算法[J].测绘科学, 2017, 42(6):171-177. HUANG Quan, LIU Hao, LIANG Pingyuan. Parallel construction algorithm of delaunay triangulated irregular network[J]. Science of Surveying and Mapping, 2017, 42(6):171-177.
[11] LEE D T, SCHACHTER B J. Two algorithms for constructing a Delaunay triangulation[J]. International Journal of Computer&Information Sciences, 1980, 9(3):219-242.
[12] 黄继风.基于Delaunay三角网的城市多边形合并算法[J].计算机工程与设计, 2004, 25(7):1220-1222. HUANG Jifeng. Urban polygon aggregation algorithms based on Delaunay trigonometry network[J]. Computer Engineering and Design, 2004,25(7):1220-1222.
[13] 钱海忠,武芳.基于Delaunay三角关系的面状要素合并方法[J].测绘学院学报, 2001, 18(3):207-209. QIAN Haizhong, WU Fang. A merge operation for area objects based on Delaunay triangle-interpolating[J]. Journal of Institute of Surveying and Mapping, 2001, 18(3):207-209.
[14] 郭沛沛,李成名,殷勇.建筑物合并的Delaunay三角网分类过滤法[J].测绘学报, 2016, 45(8):1001-1007. DOI:10.11947/j.AGCS.2016.20150587. GUO Peipei, LI Chengming, YIN Yong. Classification and filtering of constrained Delaunay triangulation for automated building aggregation[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8):1001-1007.DOI:10.11947/j.AGCS.2016.20150587.
[15] HE Xianjin, ZHANG Xinchang, YANG Jie. Progressive amalgamation of building clusters for map generalization based on scaling subgroups[J]. ISPRS International Journal of Geo-Information, 2018, 7(3):116-131.
[16] HE Xianjin, ZHANG Xinchang, XIN Qinchuan. Recognition of building group patterns in topographic maps based on graph partitioning and random forest[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136:26-40.
[17] AI Tinghua, ZHANG Xiang. The aggregation of urban building clusters based on the skeleton partitioning of gap space[M]//FABRIKANT S I, WACHOWICZ M. The European Information Society:Leading the Way with Geo-information. Berlin:Springer, 2007:153-170.
[18] 程博艳,刘强,李小文.一种建筑物群智能聚类法[J].测绘学报, 2013, 42(2):290-294, 330. CHENG Boyan, LIU Qiang, LI Xiaowen. Intelligent building grouping using a self-organizing map[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2):290-294, 330.
[19] BASARANER M, SELCUK M. A structure recognition technique in contextual generalisation of buildings and built-up areas[J]. The Cartographic Journal, 2008, 45(4):274-285.
[20] 钱海忠,武芳,谭笑,等.基于ABTM的城市建筑物合并算法[J].中国图象图形学报, 2005, 10(10):1224-1233. QIAN Haizhong, WU Fang, TAN Xiao, et al. The algorithm for merging city buildings based on ABTM[J]. Journal of Image and Graphics, 2005, 10(10):1224-1233.
[21] PENG Dongliang, TOUYA G. Continuously generalizing buildings to built-up areas by aggregating and growing[C]//Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics. Redondo Beach, CA:Association for Computing Machinery, 2017:210-225.
[22] 许文帅,龙毅,周侗,等.面向复杂多边形合并的视觉邻近探测与缝合算法[J].地理与地理信息科学, 2014, 30(1):125-126. XU Wenshuai, LONG Yi, ZHOU Dong, et al. Visual proximity detection and stitching algorithm for complex polygon merging[J]. Geography and Geo-Information Science, 2014, 30(1):125-126.
[23] 毋河海. GIS与地图信息综合基本模型与算法[M].武汉:武汉大学出版社, 2012. WU Hehai. Basic model and algorithm of GIS and map generalization[M]. Wuhan:Wuhan University Press, 2012.
[24] 郭庆胜,魏智威,王勇,等.特征分类与邻近图相结合的建筑物群空间分布特征提取方法[J].测绘学报, 2017, 46(5):631-638. DOI:10.11947/j.AGCS.2017.20160374. GUO Qingsheng, WEI Zhiwei, WANG Yong, et al. The method of extracting spatial distribution characteristics of buildings combined with feature classification and proximity graph[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5):631-638. DOI:10.11947/j.AGCS.2017.20160374.
[25] 程博艳.基于神经网络的地图建筑物要素智能综合研究[D].成都:电子科技大学, 2014. CHENG Boyan. Research on ann-based map intelligent generalization for buildings[D].Chengdu:University of Electronic Science and Technology of China, 2014.
[26] SHEN Yilang, AI Tinghua, LI Wende, et al. A polygon aggregation method with global feature preservation using superpixel segmentation[J]. Computers, Environment and Urban Systems, 2019, 75:117-131.
[27] 郭庆胜,丁虹,刘浩,等.面状目标之间空间拓扑关系的组合式分类[J].武汉大学学报(信息科学版), 2005, 30(8):728-731. GUO Qingsheng, DING Hong, LIU Hao, et al. Combinational reasoning of spatial topological relations between two areas based on basic spatial relations[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8):728-731.
[28] LI Zhilin, OPENSHAW S. Algorithms for automated line generalization based on a natural principle of objective generalization[J]. International Journal of Geographical Information Systems, 1992, 6(5):373-389.
[29] MULLER J C. Optimum point density and compaction rates for the representation of geographic lines[C]//Proceedings of 1987 International Symposium on Computer-Assisted Cartography. Baltimore, Maryland:[s.n.], 1987:221-230.