顾及功能语义特征的建筑物空间分布模式识别方法

doi:10.11947/j.AGCS.2020.20190222

测绘学报 ›› 2020, Vol. 49 ›› Issue (5): 622-631.doi: 10.11947/j.AGCS.2020.20190222

顾及功能语义特征的建筑物空间分布模式识别方法

刘慧敏, 胡文柯, 唐建波, 石岩, 邓敏

中南大学地理信息系, 湖南长沙 410083

收稿日期:2019-06-04 修回日期:2019-11-07 发布日期:2020-05-23
通讯作者: 唐建波 E-mail:jianbo.tang@csu.edu.cn
作者简介:刘慧敏(1977-),女,博士,副教授,研究方向为大数据地图制图。E-mail:lhmgis@csu.edu.cn
基金资助:
国家自然科学基金（41771492；41901406）；湖南省自然科学基金（2018JJ3639）；国家重点研发计划（2017YFB0503600）

A method for recognizing building clusters by considering functional features of buildings

LIU Huimin, HU Wenke, TANG Jianbo, SHI Yan, DENG Min

Department of Geo-informatics, Central South University, Changsha 410083, China

Received:2019-06-04 Revised:2019-11-07 Published:2020-05-23
Supported by:
The National Natural Science Foundation of China (Nos. 41771492;41901406);The Natural Science Foundation of Hunan Province(No. 2018JJ3639);The National Key Research and Development Program of China(No. 2017YFB0503600)

摘要/Abstract

摘要： 本文从空间-语义双重约束角度，提出一种顾及空间邻近和功能语义相似的建筑物空间分布模式识别方法。首先，基于建筑物的空间位置邻近性（即建筑物间的最小距离）约束进行聚类，获得建筑物的空间分布模式和建筑物间的空间邻近关系；然后，根据建筑物的功能语义相似性约束进行分割，获得建筑物的初步聚类结果；最后，考虑簇内相似性与簇间差异性进行整体优化，获得最终聚类结果。试验验证表明，本文方法比现有方法能够更有效地识别空间邻近与功能语义一致的建筑物群，服务于智慧城市建设中对建筑物进行语义层次综合和对城市结构进行深入研究的需求。

关键词: 建筑物聚类, 空间分布模式, 功能语义特征, 最小生成树, 地图综合

Abstract: From the perspective of space-semantic dual constraints, a method for building spatial distribution pattern recognition considering spatial proximity and functional similarity is proposed. Firstly, under the space-semantic divide and conquer strategy, buildings are clustering based on the spatial proximity of the building (i.e. the minimum distance between buildings), and the spatial distribution pattern of the building and the spatial proximity between the buildings are constructed. Then, the clusters are divided into partition according to the functional semantic similarity constraint of the building, the preliminary clustering results of the building are obtained. Finally, the overall clustering results are optimized based on intra-cluster similarity and inter-cluster differences. The experimental results show that the proposed method is able to recognize the building groups with spatial proximity and functional semantics similarity by comparison of the existing methods, and the recognition result is more in line with the need for semantic level generalization of buildings and research on urban structure in smart city applications.

Key words: building clustering, spatial distribution pattern, functional and semantic features, minimum spanning tree, map generalization

中图分类号:

P208

刘慧敏, 胡文柯, 唐建波, 石岩, 邓敏. 顾及功能语义特征的建筑物空间分布模式识别方法[J]. 测绘学报, 2020, 49(5): 622-631.

LIU Huimin, HU Wenke, TANG Jianbo, SHI Yan, DENG Min. A method for recognizing building clusters by considering functional features of buildings[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 622-631.

参考文献

[1] YAN Haowen, WEIBEL R, YANG Bisheng. A multi-parameter approach to automated building grouping and generalization[J]. Geoinformatica, 2008, 12(1): 73-89.
[2] 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, 303. 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, 303.
[3] WANG Wanyi, DU Shihong, GUO Zhou, et al. Polygonal clustering analysis using multilevel graph-partition[J]. Transactions in GIS, 2015, 19(5): 716-736.
[4] REGNAULD N. Contextual building typification in automated map generalization[J]. Algorithmica, 2001, 30(2): 312-333.
[5] 艾廷华, 郭仁忠. 基于格式塔识别原则挖掘空间分布模式[J]. 测绘学报, 2007, 36(3): 302-308. AI Tinghua, GUO Renzhong. Polygon cluster pattern mining based on gestalt principles[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(3): 302-308.
[6] DENG Min, LIU Qiliang, CHENG Tao, et al. An adaptive spatial clustering algorithm based on delaunay triangulation[J]. Computers, Environment and Urban Systems, 2011, 35(4): 320-332.
[7] WEI Zhiwei, GUO Qingsheng, WANG Lin, et al. On the spatial distribution of buildings for map generalization[J]. Cartography and Geographic Information Science, 2018, 45(6): 539-555.
[8] ZHANG Xiang, AI Tinghua, STOTER J, et al. Building pattern recognition in topographic data: examples on collinear and curvilinear alignments[J]. GeoInformatica, 2013, 17(1): 1-33.
[9] ZHANG Xiang, AI Tinghua, STOTER J, et al. Characterization and detection of building patterns in cartographic data: two algorithms[C]//Proceedings of the 14th International Symposium on Spatial Data Handling. Berlin: Springer, 2012: 93-107.
[10] LI Zhilin, YAN Haowen, AI Tinghua. Automated building generalization based on urban morphology and Gestalt theory[J]. International Journal of Geographical Information Science, 2004, 18(5): 513-534.
[11] ZHANG Liqiang, DENG Hao, CHEN Dong, et al. A spatial cognition-based urban building clustering approach and its applications[J]. International Journal of Geographical Information Science, 2013, 27(4): 721-740.
[12] DENG Min, TANG Jianbo, LIU Qiliang, et al. Recognizing building groups for generalization: a comparative study[J]. Cartography and Geographic Information Science, 2018, 45(3): 187-204.
[13] 刘慧敏, 邓敏, 樊子德, 等. 地图上居民地空间信息的特征度量法[J]. 测绘学报, 2014, 43(10): 1092-1098. DOI: 10.13485/j.cnki.11-2089.2014.0154. LIU Huimin, DENG Min, FAN Zide, et al. A characteristics-based approach to measuring spatial information content of the settlements in a map[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(10): 1092-1098. DOI: 10.13485/j.cnki.11-2089.2014.0154.
[14] LI Zhilin. Algorithmic foundation of multi-scale spatial representation[M]. Boca Raton: Taylor & Francis Group, 2007: 122-125.
[15] CHEN Yimin, LIU Xiaoping, LI Xia, et al. Delineating urban functional areas with building-level social media data: a dynamic time warping (DTW) distance based k-medoids method[J]. Landscape and Urban Planning, 2017, 160: 48-60.
[16] TSAI V J D. Delaunay triangulations in TIN creation: an overview and a linear-time algorithm[J]. International Journal of Geographical Information Systems, 1993, 7(6): 501-524.
[17] WU Bin, YU Bailang, WU Qiusheng, et al. An extended minimum spanning tree method for characterizing local urban patterns[J]. International Journal of Geographical Information Science, 2018, 32(3): 450-475.
[18] PRIM R C. Shortest connection networks and some generalizations[J]. The Bell System Technical Journal, 1957, 36(6): 1389-1401.
[19] DU Shihong, ZHANG Fangli, ZHANG Xiuyuan. Semantic classification of urban buildings combining VHR image and GIS data: an improved random forest approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 107-119.
[20] YUAN Jing, ZHENG Yu, XIE Xing. Discovering regions of different functions in a city using human mobility and POIs[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing: ACM, 2012: 186-194.
[21] 刘菊, 许珺, 蔡玲, 等. 基于出租车用户出行的功能区识别[J]. 地球信息科学学报, 2018, 20(11): 1550-1561. LIU Ju, XU Jun, CAI Lin, et al. Identifying functional regions based on the spatio-temporal pattern of taxi trajectories[J]. Journal of Geo-information Science, 2018, 20(11): 1550-1561.
[22] 谷岩岩, 焦利民, 董婷, 等. 基于多源数据的城市功能区识别及相互作用分析[J]. 武汉大学学报(信息科学版), 2018, 43(7): 1113-1121. GU Yanyan, JIAO Limin, DONG Ting, et al. Spatial distribution and interaction analysis of urban functional areas based on multi-source data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1113-1121.
[23] 蒋云良, 董墨萱, 范婧, 等. 基于POI数据的城市功能区识别方法研究[J]. 浙江师范大学学报(自然科学版), 2017, 40(4): 398-405. JIANG Yunliang, DONG Moxuan, FAN Jing, et al. Research on identifying urban regions of different functions based on POI data[J]. Journal of Zhejiang Normal University (Natural Sciences), 2017, 40(4): 398-405.
[24] 刘启亮, 邓敏, 石岩, 等. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011, 40(4): 509-516. LIU Qiliang, DENG Min, SHI Yan, et al. A novel spatial clustering method based on multi-constraints[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4): 509-516.
[25] 刘启亮. 自适应空间聚类方法研究[D]. 长沙: 中南大学, 2011. LIU Qiliang. A methodology of adaptive spatial clustering analysis[D]. Changsha: Central South University, 2011.

顾及功能语义特征的建筑物空间分布模式识别方法

A method for recognizing building clusters by considering functional features of buildings

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