Building typification in map generalization using affinity propagation clustering

Building typification is of theoretical interest and practical significance in map general-ization. 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 lo-cated...

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Main Authors: Yan, Xiongfeng, Chen, Huan, Huang, Haoran, Liu, Qian, Yang, Min
Other Authors: Nanyang Environment and Water Research Institute
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/153895
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1538952022-05-28T20:11:28Z Building typification in map generalization using affinity propagation clustering Yan, Xiongfeng Chen, Huan Huang, Haoran Liu, Qian Yang, Min Nanyang Environment and Water Research Institute Engineering::Environmental engineering Building Typification Exemplar-Based Clustering Building typification is of theoretical interest and practical significance in map general-ization. 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 lo-cated 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. Published version This research was funded by the National Natural Science Foundation of China, grant numbers 42071450, 42001415. 2022-05-24T01:52:06Z 2022-05-24T01:52:06Z 2021 Journal Article Yan, X., Chen, H., Huang, H., Liu, Q. & Yang, M. (2021). Building typification in map generalization using affinity propagation clustering. ISPRS International Journal of Geo-Information, 10(11), 732-. https://dx.doi.org/10.3390/ijgi10110732 2220-9964 https://hdl.handle.net/10356/153895 10.3390/ijgi10110732 2-s2.0-85118997990 11 10 732 en ISPRS International Journal of Geo-Information © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Environmental engineering
Building Typification
Exemplar-Based Clustering
spellingShingle Engineering::Environmental engineering
Building Typification
Exemplar-Based Clustering
Yan, Xiongfeng
Chen, Huan
Huang, Haoran
Liu, Qian
Yang, Min
Building typification in map generalization using affinity propagation clustering
description Building typification is of theoretical interest and practical significance in map general-ization. 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 lo-cated 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.
author2 Nanyang Environment and Water Research Institute
author_facet Nanyang Environment and Water Research Institute
Yan, Xiongfeng
Chen, Huan
Huang, Haoran
Liu, Qian
Yang, Min
format Article
author Yan, Xiongfeng
Chen, Huan
Huang, Haoran
Liu, Qian
Yang, Min
author_sort Yan, Xiongfeng
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
publishDate 2022
url https://hdl.handle.net/10356/153895
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