建筑的分类和聚类,以了解城市动态

IF 0.2 Q4 REMOTE SENSING Revue Internationale de Geomatique Pub Date : 2022-07-01 DOI:10.3166/rig31.303-328
Joan Perez, Giovanni Fusco, Y. Sadahiro
{"title":"建筑的分类和聚类,以了解城市动态","authors":"Joan Perez, Giovanni Fusco, Y. Sadahiro","doi":"10.3166/rig31.303-328","DOIUrl":null,"url":null,"abstract":"This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed, as well as the differences between each period. Mappings are then performed, showing that building types have specific locations and dynamics. In some extreme cases, a specific building type can even gradually replace a type on a declining dynamic. Questions of data preparation, and clustering validation are also dealt with, underlining the interest of assessing the spatial distribution of clusters.","PeriodicalId":41172,"journal":{"name":"Revue Internationale de Geomatique","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and clustering of buildings for understanding urban dynamics\",\"authors\":\"Joan Perez, Giovanni Fusco, Y. Sadahiro\",\"doi\":\"10.3166/rig31.303-328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed, as well as the differences between each period. Mappings are then performed, showing that building types have specific locations and dynamics. In some extreme cases, a specific building type can even gradually replace a type on a declining dynamic. Questions of data preparation, and clustering validation are also dealt with, underlining the interest of assessing the spatial distribution of clusters.\",\"PeriodicalId\":41172,\"journal\":{\"name\":\"Revue Internationale de Geomatique\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revue Internationale de Geomatique\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3166/rig31.303-328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revue Internationale de Geomatique","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3166/rig31.303-328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了在建筑层面研究城市动力学的不同方法。建筑类型是在一个基于矢量的综合建筑清单中确定的,至少跨越两个时间点。首先,计算每栋建筑的基本形态指标:面积、建筑面积、邻居数量、延伸率和凸度。基于专家知识的可用性,进行了不同类型的分类和聚类:监督树状分类模型、专家约束k-均值和组合SOM-HCA。将网格叠加在大阪(日本)的测试区域上,计算每个单元和每个周期的建筑类型数量,以及每个周期之间的差异。然后执行映射,显示建筑类型具有特定的位置和动力学。在某些极端情况下,特定的建筑类型甚至可以逐渐取代动态下降的类型。还讨论了数据准备和聚类验证问题,强调了评估聚类空间分布的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Classification and clustering of buildings for understanding urban dynamics
This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed, as well as the differences between each period. Mappings are then performed, showing that building types have specific locations and dynamics. In some extreme cases, a specific building type can even gradually replace a type on a declining dynamic. Questions of data preparation, and clustering validation are also dealt with, underlining the interest of assessing the spatial distribution of clusters.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Extraction et mise en contexte spatial des propositions relatives au transport dans le Grand Débat National Editorial Spatio-temporal pattern detection in spatio-temporal graphs Classification and clustering of buildings for understanding urban dynamics Building types in France
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1