UrbanClassifier: A deep learning-based model for automated typology and temporal analysis of urban fabric across multiple spatial scales and viewpoints

Abstract

The field of urban morphology, crucial for understanding the evolutionary trajectories of cityscapes, has traditionally depended on manual classification methods. The surge in deep learning and computer vision technologies presents an opportunity to automate and enhance urban typo-morphology studies. This research addresses three critical shortcomings in the current body of work: the neglect of urban fabric's three-dimensional qualities, the homogeneity of spatial scales in dataset creation and the dependence on a single-perspective for urban fabric classification. A novel deep learning-based model, UrbanClassifier, is introduced, trained on a substantial dataset that encapsulates the three-dimensionality of urban fabric along with morphological types and development periods. Extensive experimentation across four European cities highlights the model's ability to incorporate diverse spatial scales and viewpoints in urban fabric analysis. The UrbanClassifier exemplifies a method integrating features from various scales and perspectives, thus laying the groundwork for scalable and accessible urban typo-morphology studies, aiding practitioners in discerning the spatio-temporal evolution of urban fabric.