DeepStreet: A deep learning powered urban street network generation module
This provides a novel tool for urban designers to efficiently generate street networks that maintain consistency with existing layouts, particularly useful in rapidly developing cities.
The study tackled the problem of rapid urban street network design by developing DeepStreet, a deep learning module that predicts street network expansion patterns based on local features, achieving the ability to detect and cluster complex street patterns and predict both gridiron and irregular networks in Barcelona.
In countries experiencing unprecedented waves of urbanization, there is a need for rapid and high quality urban street design. Our study presents a novel deep learning powered approach, DeepStreet (DS), for automatic street network generation that can be applied to the urban street design with local characteristics. DS is driven by a Convolutional Neural Network (CNN) that enables the interpolation of streets based on the areas of immediate vicinity. Specifically, the CNN is firstly trained to detect, recognize and capture the local features as well as the patterns of the existing street network sourced from the OpenStreetMap. With the trained CNN, DS is able to predict street networks' future expansion patterns within the predefined region conditioned on its surrounding street networks. To test the performance of DS, we apply it to an area in and around the Eixample area in the City of Barcelona, a well known example in the fields of urban and transport planning with iconic grid like street networks in the centre and irregular road alignments farther afield. The results show that DS can (1) detect and self cluster different types of complex street patterns in Barcelona; (2) predict both gridiron and irregular street and road networks. DS proves to have a great potential as a novel tool for designers to efficiently design the urban street network that well maintains the consistency across the existing and newly generated urban street network. Furthermore, the generated networks can serve as a benchmark to guide the local plan-making especially in rapidly developing cities.