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

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  • Jinmo Rhee
  • Ramesh Krishnamurti

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.

Suggested Citation

  • Jinmo Rhee & Ramesh Krishnamurti, 2024. "An inductive method for classifying building form in a city with implications for orientation," Environment and Planning B, , vol. 51(8), pages 1814-1832, October.
  • Handle: RePEc:sae:envirb:v:51:y:2024:i:8:p:1814-1832
    DOI: 10.1177/23998083231224505
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    References listed on IDEAS

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    1. Chenyi Cai & Zifeng Guo & Baizhou Zhang & Xiao Wang & Biao Li & Peng Tang, 2021. "Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    2. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
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