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

• 地图学与地理信息 • 上一篇    下一篇

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   

  1. 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   

  1. 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: 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

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