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A Deep Learning Approach for Urban Block: Automated Extraction Tool for Urban Forms
(Lodz University of Technology Press, 2023) Turk Didem
Increasing access to geographic data and mapping technologies has pushed urban morphology research toward more quantitative and data-driven approaches. At the same time, the unprecedented rapid change in the urban form has prompted a growing number of research to capture, analyze, and understand the phenomenon in recent years. However, a thorough, systematic approach to evaluating and comparing urban forms in this setting is yet to be developed. The aim of this study is to build a comprehensive approach to defining urban form indicators by developing a simplified yet representative classification of the urban form. Notably, urban block as a constitutional feature of urban form is evaluated in relation to numerical indices. The applied methodology comprises the detection and classification of urban form using a deep convolutional neural network. The study attempts to use automated methods to address the gap in urban form classification and characterization. The methodological process encompasses a non-local classification of urban form, followed by an examination of the identified features of the urban block. The preliminary outcome of this study consists of an in-depth analysis of urban block indicators in the comparative literature. This will be one of the inputs of the deep learning model to classify urban blocks.