Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153895
Title: Building typification in map generalization using affinity propagation clustering
Authors: Yan, Xiongfeng
Chen, Huan
Huang, Haoran
Liu, Qian
Yang, Min
Keywords: Engineering::Environmental engineering
Issue Date: 2021
Source: 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
Journal: ISPRS International Journal of Geo-Information 
Abstract: 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.
URI: https://hdl.handle.net/10356/153895
ISSN: 2220-9964
DOI: 10.3390/ijgi10110732
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NEWRI Journal Articles

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