"Reading" cities with computer vision: a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks

This paper builds on the proven track record of CNN-based pattern recognition and feature extraction methods, and reports a novel model that classifies urban fabric samples of metropolitan areas in terms of (1) which city they belong to, (2) what types of urban fabric they belong to, and (3) which historic period they originate from. Currently, such tasks require intensive manual work by senior professionals, and even then, inconsistencies and errors occur. Our work is based on a novel urban fabric dataset of four metropolitan areas with distinct typologies (linear development, open block, gated compound, medieval region, irregular grid and orthogonal gird), which consist of high resolution 3-dimensional built form data and hierarchical street networks. The classification model presented in this paper is the first that is capable of predicting the city origin, urban fabric pattern type and construction period. The novelty is also characterised by jointly considering urban fabric features across multiple spatial scales. The experiments demonstrate that this multi-scale approach can capture a wide range of urban fabric features across cities, urban fabric pattern types and development periods. We further find that the effectiveness can be enhanced by appending an auxiliary network for identifying the most appropriate combinations of the multiple spatial scales in line with the classification task. The dataset and model can massively scale up the productivity of researchers and professionals working on cities.