A method for automatic buildings aggregation constrained by proximity edges

doi:10.11947/j.AGCS.2025.20240138

Abstract

Abstract:

Aggregation of large scale buildings is a difficult problem in the field of map generalization. In order to maintain the characteristic consistency of the building's shape before and after the aggregation, a method for automatic buildings aggregation constrained by proximity edges was proposed in this paper. The method firstly used the Delaunay triangulation networks to determine the proximity relations between buildings, generated the proximity edges of buildings with the constraint of the minimum bounding rectangle of buildings, and divided the spatial structural relations of buildings into the alignment types and the dislocation types according to the projection proportions of the minimum external rectangles of buildings. Secondly, the proximity edges interactive projection method and the proximity edges angular bisector method were proposed for the combination of the aligned and the dislocated buildings, respectively. Finally, the validity of the proposed method was verified with the experimental data of Shanghai residential land. The results showed that the proposed method could effectively merge buildings under different structural relations and merging thresholds, and effectively expressed the spatial structural characteristics and the right-angle characteristics of residential lands under the control of a certain area increment.

Key words: map generalization, building aggregation, proximity edges, spatial structure, multi-scale expression

CLC Number:

P208

Youneng SU, Qing XU, Qun SUN, Xinming ZHU, Fubing ZHANG, Bo LIU. A method for automatic buildings aggregation constrained by proximity edges[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(3): 563-576.

Figures/Tables 23

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Tab. 1

Tab. 2

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Tab. 3

Tab. 4

Fig. 18

Fig. 19

References 35

[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]	胡慧明, 钱海忠, 何海威, 等. 采用层次分析法的面状居民地自动选取[J]. 测绘学报, 2016, 45(6): 740-746,755. DOI:. doi: 10.11947/j.AGCS.2016.20150078
	HU Huiming, QIAN Haizhong, HE Haiwei, et al. Auto-selection of areal habitation based on analytic hierarchy process[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(6): 740-746,755. DOI:. doi: 10.11947/j.AGCS.2016.20150078
[3]	王家耀. 地图集：重构复杂非线性地理世界的“百科全书”[J]. 测绘地理信息, 2021, 46(1): 1-8.
	WANG Jiayao. Atlas: the “Encyclopedia” of reconstructing complex nonlinear geographical world[J]. Journal of Geomatics, 2021, 46(1): 1-8.
[4]	尹烁, 闫小明, 晏雄锋. 基于特征边重构的建筑物化简方法[J]. 测绘学报, 2020, 49(6): 703-710. DOI:. doi: 10.11947/j.AGCS.2020.20190299
	YIN Shuo, YAN Xiaoming, YAN Xiongfeng. Simplification method of building polygon based on feature edges reconstruction[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 703-710. DOI:. doi: 10.11947/j.AGCS.2020.20190299
[5]	唐曾杨, 艾廷华, 徐海江. 地理知识图谱下的建筑群空间分布模式推理[J]. 地球信息科学学报, 2023, 25(6): 1202-1214.
	TANG Zengyang, AI Tinghua, XU Haijiang. Reasoning of spatial distribution pattern of building cluster based on geographic knowledge graph[J]. Journal of Geo-Information Science, 2023, 25(6): 1202-1214.
[6]	魏智威, 肖屹, 童莹, 等. 基于知识图谱的直线型建筑物模式识别方法[J]. 测绘学报, 2023, 52(8): 1355-1363. DOI:. doi: 10.11947/j.AGCS.2023.20220121
	WEI Zhiwei, XIAO Yi, TONG Ying, et al. Linear building pattern recognition via spatial knowledge graph[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1355-1363. DOI:. doi: 10.11947/j.AGCS.2023.20220121
[7]	朵天林. 居民地综合决策模型和方法研究[D]. 郑州: 信息工程大学, 2020.
	DUO Tianlin. Research on decision model and method of cartographic generalization of residential areas[D]. Zhengzhou: Information Engineering University, 2020.
[8]	DU S, LUO L, CAO K, et al. Extracting building patterns with multilevel graph partition and building grouping[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122: 81-96.
[9]	YAN X, AI T, YANG M, et al. A graph convolutional neural network for classification of building patterns using spatial vector data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 259-273.
[10]	WANG X, BURGHARDT D. A typification method for linear building groups based on stroke simplification[J]. Geocarto International, 2021, 36(15): 1732-1751.
[11]	行瑞星, 武芳, 巩现勇, 等. 建筑群组合直线模式识别的模板匹配方法[J]. 测绘学报, 2021, 50(6): 800-811. DOI:. doi: 10.11947/j.AGCS.2021.20200298
	XING Ruixing, WU Fang, GONG Xianyong, et al. The template matching approach to combined collinear pattern recognition in building groups[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(6): 800-811. DOI:. doi: 10.11947/j.AGCS.2021.20200298
[12]	HE X, ZHANG X, XIN Q. 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.
[13]	WANG H, XU Y, HU A, et al. Building pattern recognition by using an edge-attention multi-head graph convolutional network[J]. International Journal of Geographical Information Science, 2018(32): 1-26.
[14]	WEI Z, XU W, XIAO Y, et al. Enhancing building pattern recognition through multi-scale data and knowledge graph: a case study of C-shaped patterns[J]. International Journal of Digital Earth, 2023, 16(1): 3860-3881.
[15]	王光霞, 杨培. 数学形态学在居民地街区合并中的应用[J]. 测绘学院学报, 2000(3): 201-203,206.
	WANG Guangxia, YANG Pei. Application of mathematic morphology in uniting blocks of residential area[J]. Journal of Institute of Surveying and Mapping, 2000(3): 201-203,206.
[16]	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(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(1): 25-30.
[17]	SHEN Y, AI T, LI W, et al. A polygon aggregation method with global feature preservation using superpixel segmentation[J]. Computers, Environment and Urban Systems, 2019, 75: 117-131.
[18]	SESTER M, FENG Y, THIEMANN F. Building generalization using deep learning[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, XLII-4: 565-572.
[19]	李安平, 翟仁健, 殷吉崇, 等. 顾及空间结构关系的居民地自动合并方法[J]. 武汉大学学报(信息科学版), 2024, 49(9): 1723-1733.
	LI Anping, ZHAI Renjian, YIN Jichong, et al. Automatic aggregation of building considering the spatial structure[J]. Geomatics and Information Science of Wuhan University, 2024, 49(9): 1723-1733.
[20]	PENG D, 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: ACM Press, 2017: 1-8.
[21]	王勇, 罗安, 王慧慧, 等. 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. DOI:. doi: 10.11947/j.AGCS.2021.20200493
	WANG Yong, LUO An, WANG Huihui, et al. A method of polygon aggregation for complex buildings based on shortest adjacent lines[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12): 1671-1682. DOI:. doi: 10.11947/j.AGCS.2021.20200493
[22]	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. DOI:. doi: 10.11947/j.AGCS.2024.20230316
	CHEN Zhanlong, LU Xiechun, XU Yongyang. A building aggregation method based on deep clustering of graph vertices[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 736-749. DOI:. doi: 10.11947/j.AGCS.2024.20230316
[23]	AI T, YIN H, SHEN Y, et al. A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation[J]. PLOS ONE, 2019, 14(7): e0218877.
[24]	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:. 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:. doi: 10.11947/j.AGCS.2016.20150587
[25]	张巧凤. 应用Delaunay三角网进行城市居民地和路网自动综合理论和方法研究[D]. 太原: 太原理工大学, 2005.
	ZHANG Qiaofeng. Study on the theory and method of automatic synthesis of urban residential land and road network by Delaunay triangulation[D]. Taiyuan: Taiyuan University of Technology, 2005.
[26]	USUI H, TERAKI A, OKUNUKI KI, et al. A comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings[J]. International Journal of Geographical Information Science, 2020, 34(11): 2177-2203.
[27]	ZHANG F, SUN Q, MA J, et al. A polygonal buildings aggregation method considering obstacle elements and visual clarity[J]. Geocarto International, 2023, 38(1): 2266672.
[28]	REGNAULD N. Algorithms for the amalgamation of topographic data[C]//Proceedings of the 21st International Cartographic Conference. Durban: [s.n.], 2003.
[29]	HE X, ZHANG X, YANG J. Progressive amalgamation of building clusters for map generalization based on scaling subgroups[J]. ISPRS International Journal of Geo-Information, 2018, 7(3): 116.
[30]	WANG L, ZHANG J, YAO G. The analysis and experiments of least-squares method for settlements generalization[C]//Proceedings of 2012 IEEE International Conference on Information Science and Technology. Wuhan: IEEE, 2012: 210-214.
[31]	苏友能, 徐青, 朱新铭, 等. POI点要素空间邻近图质量评价——以Delaunay三角网和缓冲区构建方法为例[J]. 地理与地理信息科学, 2024, 40(3): 14-20.
	SU Youneng, XU Qing, ZHU Xinming, et al. Quality evaluation on spatial proximity graph of POI based on Delaunay triangulation network and buffer construction method[J]. Geography and Geo-Information Science, 2024, 40(3): 14-20.
[32]	巩现勇, 武芳. 城市建筑群网格模式的图论识别方法[J]. 测绘学报, 2014, 43(9): 960-968. DOI:. doi: 10.13485/j.cnki.11-2089.2014.0125
	GONG Xianyong, WU Fang. The graph theory approach to grid pattern recognition in urban building groups[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(9): 960-968. DOI:. doi: 10.13485/j.cnki.11-2089.2014.0125
[33]	肖天元, 艾廷华, 余华飞, 等. 地图综合图卷积神经网络点群简化方法[J]. 测绘学报, 2024, 53(1): 158-172. DOI:. doi: 10.11947/j.AGCS.2024.20220584
	XIAO Tianyuan, AI Tinghua, YU Huafei, et al. A point cluster simplification approach of graph convolutional neural network for map generalization[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(1): 158-172. DOI:. doi: 10.11947/j.AGCS.2024.20220584
[34]	全国地理信息标准化技术委员会. 国家基本比例尺地图图式第3部分：1∶25 000 1∶50 000 1∶100 000地形图图式：GB/T 20257.3—2017[S]. 北京: 中国标准出版社, 2017.
	National Technical Committee for Geographic Information Standardization. Cartographic symbols for national fundamental scale maps: part 3: specifications for cartographic symbols 1∶25 000 1∶50 000 & 1∶100 000 topographic maps: GB/T 20257.3—2017[S]. Beijing: Standards Press of China, 2017.
[35]	张秀红, 陈迪, 刘纪平, 等. 结构化居民地群的多层次识别方法[J]. 武汉大学学报(信息科学版), 2018, 43(8): 1144-1151.
	ZHANG Xiuhong, CHEN Di, LIU Jiping, et al. A multilevel identification approach to structured building clusters[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1144-1151.

类别	邻近边上投影点数量	是否构建桥接面
Ⅰ	2	是
Ⅱ	2	是
Ⅲ	3	是
Ⅳ	4	是

类别	邻近边上交点数量	是否构建桥接面
Ⅰ	0	否
Ⅱ	1	是
Ⅲ	2	是
Ⅳ	1	是
Ⅴ	0	是

试验	建筑物数量	目标比例尺	总面积/m²	直角占比/（%）	建筑物数量变化率/（%）	建筑物面积变化率/（%）
试验1	327	1∶2.5万	565 825.72	75.44	-50.00	7.41
试验2	177	1∶5万	663 545.11	68.59	-72.94	25.96

试验方法	建筑物数量	建筑物面积/m²	建筑物直角占比/（%）	建筑物数量变化率/（%）	建筑物面积变化率/（%）
本文方法	45	255 539.68	84.12	-81.14	39.03
Arc Map方法	82	247 399.73	82.31	-64.04	34.60
文献[19]方法	53	250 345.93	77.39	-76.75	36.20
文献[24]方法	54	247 482.89	81.49	-76.32	34.65
文献[27]方法	65	252 183.39	81.17	-71.49	37.20