Alessandro Araldi1, David Emsellem2, Giovanni Fusco1, Andrea Tettamanzi3, Denis Overal2
Revue Internationale de Géomatique, Vol.31, No.2, pp. 265-302, 2022, DOI:10.3166/rig31.265-302
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 More >
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