An inductive method for classifying building form in a city with implications for orientation

Jinmo Rhee and Ramesh Krishnamurti

Environment and Planning B, 2024, vol. 51, issue 8, 1814-1832

Abstract: The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.

Keywords: Building form; deep learning; form classification; orientation; form data (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:51:y:2024:i:8:p:1814-1832

DOI: 10.1177/23998083231224505

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