Allessandro Araldi, David Emsellem, Giovanni Fusco, A. Tettamanzi, Denis Overal
{"title":"法国的建筑类型","authors":"Allessandro Araldi, David Emsellem, Giovanni Fusco, A. Tettamanzi, Denis Overal","doi":"10.3166/rig31.265-302","DOIUrl":null,"url":null,"abstract":"The identification and description of building typologies play a fundamental role in the understanding of the overall built-up form. A growing body of research is developing and implementing sophisticated, computer-aided protocols for the identification of building typologies. This paper shares the same goal. An innovative data-driven procedure for the unsupervised identification and description of building types and organization is here presented. After a specific pre-processing procedure, we develop an unsupervised clustering combining a new algorithm of Naive Bayes inference and hierarchical ascendant approaches relying on six morphometric features of buildings. This protocol allows us to identify groups of buildings sharing specific similar morphological characteristics and their overall structure at different aggregation levels. The proposed methodology is implemented and evaluated on the overall ordinary (e.g. not-specialized) building stock of France.","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\":\"Building types in France\",\"authors\":\"Allessandro Araldi, David Emsellem, Giovanni Fusco, A. Tettamanzi, Denis Overal\",\"doi\":\"10.3166/rig31.265-302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The identification and description of building typologies play a fundamental role in the understanding of the overall built-up form. A growing body of research is developing and implementing sophisticated, computer-aided protocols for the identification of building typologies. This paper shares the same goal. An innovative data-driven procedure for the unsupervised identification and description of building types and organization is here presented. After a specific pre-processing procedure, we develop an unsupervised clustering combining a new algorithm of Naive Bayes inference and hierarchical ascendant approaches relying on six morphometric features of buildings. This protocol allows us to identify groups of buildings sharing specific similar morphological characteristics and their overall structure at different aggregation levels. The proposed methodology is implemented and evaluated on the overall ordinary (e.g. not-specialized) building stock of France.\",\"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/rig31.265-302\",\"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/rig31.265-302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
The identification and description of building typologies play a fundamental role in the understanding of the overall built-up form. A growing body of research is developing and implementing sophisticated, computer-aided protocols for the identification of building typologies. This paper shares the same goal. An innovative data-driven procedure for the unsupervised identification and description of building types and organization is here presented. After a specific pre-processing procedure, we develop an unsupervised clustering combining a new algorithm of Naive Bayes inference and hierarchical ascendant approaches relying on six morphometric features of buildings. This protocol allows us to identify groups of buildings sharing specific similar morphological characteristics and their overall structure at different aggregation levels. The proposed methodology is implemented and evaluated on the overall ordinary (e.g. not-specialized) building stock of France.