This dissertation develops a new modelling system based on deep learning for the study and design of street network configurations. It aims to fill an existing gap in the use of deep learning in this field, through hypothesising that convolutional neural network (CNN) and generative adversarial network (GAN) models can make a revolutionary breakthrough (like in many geometry and image-intensive fields) if a modelling system is designed to suit the nature of street network configurations and the processes for designing them.

This new modelling system consists of three components. First, a thirteen-channel urban fabric image generation module, UrbanFramer, that provides a new way to represent the physical urban fabric as image patches. By integrating datasets of high-resolution 3D built form, road network data and topography of the landform, this module generates image patches as inputs for urban fabric classification and street network generation.

Secondly, an urban fabric classification module, UrbanClassifier, which takes the image patches from UrbanFramer and classifies them into morphology types using a CNN. The novelty is characterised by a unique approach of assimilating urban fabric features across multiple spatial scales and viewpoints. Furthermore, a transfer learning process in this module provides a semi-automatic way to expand and incorporate specialist planning knowledge when labelling the image patches for deep learning-based street network generation.

Thirdly, a street network design module, StreetGEN, which combines human and machine intelligence to support city-specific, contextual street network configuration design. The core of the machine intelligence consists of two GAN models, PlanStreet and TopoStreet, which learn from existing street network configuration samples and provide near-real-time design suggestions that incorporate, respectively, human guidance and local topographic conditions. Users are able to progressively and iteratively input their domain knowledge and creativity in street network design. StreetGEN turns the existing one-way, learning-based street network image generation into iterative human-computer interaction.

The above modules have been put through systematic tests using purpose-built datasets created by UrbanFramer in two groups of case study areas: Amsterdam, Barcelona, Berlin and Prague in the development of UrbanClassifier, PlanStreet, and Siena, Perugia, Rome and Florence in the development of TopoStreet.

The case study tests produce four key findings regarding the capabilities and potential of deep learning. First, equipped with multiple spatial scales and viewpoints, UrbanClassifier has very significantly improved the predictive performance, outperforming the hitherto state-of-art benchmark CNN model for image classification on all three urban fabric classification tasks by 0.14, 0.22 and 0.24 in terms of F1-Score.

Secondly, through transfer learning, UrbanClassifier has performed urban fabric classification for new cities with much reduced needs of manual labelling for training. When transferring to a new city, UrbanClassifier only requires 25% of labelled data from a new city to attain what is achieved by it directly trained on full data. In other words, with UrbanClassifier, manual labelling required for classifying new cities can be significantly reduced. The expanded dataset, in turn, offers the potential to build an ever-growing database of real-world street network configurations for deep learning.

Thirdly, by introducing a modest amount of human guidance on road junction locations and pattern types, PlanStreet has shown to be far more capable of reproducing the ground truth street networks than in an automatic-only prediction mode. Taking less than 40% of the ground truth junction information as input guidance, PlanStreet is able to predict street configurations at the same level of precision as that of existing learning-based models with 100% of the ground truth junction information.

Fourthly, by introducing local topography, TopoStreet has outperformed benchmark models for cities in hilly areas, by sharply narrowing the prediction performance differences between flat and hilly areas, from 17.02% to 3.56% in Absolute Percentage Error for Length-Weighted Metric Choice.

In summary, the design and use of this modelling system have proven that CNN and GAN models can make a real contribution to the geometrical modelling and design of street networks. This fills an existing gap in this field. Furthermore, the modelling system has designed and tested a human-machine interaction process that enables professionals and laypersons to test, near real-time, alternative designs that follow and respect local planning and topography contexts. This opens up practical applications of deep learning in the field of street network configuration design.