Building Typification in Map Generalization Using Affinity Propagation Clustering

Building typification is of theoretical interest and practical significance in map generalization. It aims to transform an initial set of buildings to a subset, while maintaining the essential distribution characteristics and important individual buildings. This study focuses on buildings located in...

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Autores principales:	Xiongfeng Yan, Huan Chen, Haoran Huang, Qian Liu, Min Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	building typification exemplar-based clustering affinity propagation distribution characteristic Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/f50b31e7effa454ca70d35a2bd30f109
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id	oai:doaj.org-article:f50b31e7effa454ca70d35a2bd30f109
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spelling	oai:doaj.org-article:f50b31e7effa454ca70d35a2bd30f1092021-11-25T17:52:48ZBuilding Typification in Map Generalization Using Affinity Propagation Clustering10.3390/ijgi101107322220-9964https://doaj.org/article/f50b31e7effa454ca70d35a2bd30f1092021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/732https://doaj.org/toc/2220-9964Building typification is of theoretical interest and practical significance in map generalization. It aims to transform an initial set of buildings to a subset, while maintaining the essential distribution characteristics and important individual buildings. This study focuses on buildings located in residential suburban or rural areas and generalizes them to medium or small scale, for which the typification process can be viewed as point-similar object selection that generates exemplars in local building clusters. From this view, we propose a novel building typification approach using affinity propagation exemplar-based clustering. Based on a sparse graph constructed on the input building set, the proposed approach considers all buildings as potential cluster exemplars and keeps passing messages between those objects; thus, high-quality representative objects (i.e., exemplars) of the initial building set can be obtained and further outputted as the typified result. Experiments with real-life building data show that the proposed method is superior to the two existing representative methods in maintaining the overall distribution characteristics. Meanwhile, the importance of each individual building and the constraints of the road network can be embedded flexibly in this method, which gives some advantages in terms of preserving important buildings and the local structural distribution along the road, etc.Xiongfeng YanHuan ChenHaoran HuangQian LiuMin YangMDPI AGarticlebuilding typificationexemplar-based clusteringaffinity propagationdistribution characteristicGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 732, p 732 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	building typification exemplar-based clustering affinity propagation distribution characteristic Geography (General) G1-922
spellingShingle	building typification exemplar-based clustering affinity propagation distribution characteristic Geography (General) G1-922 Xiongfeng Yan Huan Chen Haoran Huang Qian Liu Min Yang Building Typification in Map Generalization Using Affinity Propagation Clustering
description	Building typification is of theoretical interest and practical significance in map generalization. It aims to transform an initial set of buildings to a subset, while maintaining the essential distribution characteristics and important individual buildings. This study focuses on buildings located in residential suburban or rural areas and generalizes them to medium or small scale, for which the typification process can be viewed as point-similar object selection that generates exemplars in local building clusters. From this view, we propose a novel building typification approach using affinity propagation exemplar-based clustering. Based on a sparse graph constructed on the input building set, the proposed approach considers all buildings as potential cluster exemplars and keeps passing messages between those objects; thus, high-quality representative objects (i.e., exemplars) of the initial building set can be obtained and further outputted as the typified result. Experiments with real-life building data show that the proposed method is superior to the two existing representative methods in maintaining the overall distribution characteristics. Meanwhile, the importance of each individual building and the constraints of the road network can be embedded flexibly in this method, which gives some advantages in terms of preserving important buildings and the local structural distribution along the road, etc.
format	article
author	Xiongfeng Yan Huan Chen Haoran Huang Qian Liu Min Yang
author_facet	Xiongfeng Yan Huan Chen Haoran Huang Qian Liu Min Yang
author_sort	Xiongfeng Yan
title	Building Typification in Map Generalization Using Affinity Propagation Clustering
title_short	Building Typification in Map Generalization Using Affinity Propagation Clustering
title_full	Building Typification in Map Generalization Using Affinity Propagation Clustering
title_fullStr	Building Typification in Map Generalization Using Affinity Propagation Clustering
title_full_unstemmed	Building Typification in Map Generalization Using Affinity Propagation Clustering
title_sort	building typification in map generalization using affinity propagation clustering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f50b31e7effa454ca70d35a2bd30f109
work_keys_str_mv	AT xiongfengyan buildingtypificationinmapgeneralizationusingaffinitypropagationclustering AT huanchen buildingtypificationinmapgeneralizationusingaffinitypropagationclustering AT haoranhuang buildingtypificationinmapgeneralizationusingaffinitypropagationclustering AT qianliu buildingtypificationinmapgeneralizationusingaffinitypropagationclustering AT minyang buildingtypificationinmapgeneralizationusingaffinitypropagationclustering
_version_	1718411899041742848

Building Typification in Map Generalization Using Affinity Propagation Clustering

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