@Article{rig31.265-302,
AUTHOR = {Alessandro Araldi, David Emsellem, Giovanni Fusco, Andrea Tettamanzi, Denis Overal},
TITLE = {Clustering building morphometrics using national spatial  data},
JOURNAL = {Revue Internationale de Géomatique},
VOLUME = {31},
YEAR = {2022},
NUMBER = {2},
PAGES = {265--302},
URL = {http://www.techscience.com/RIG/v31n2/55784},
ISSN = {2116-7060},
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. notspecialized) building stock of France.},
DOI = {10.3166/rig31.265-302}
}