DeepStreet: A deep learning powered urban street network generation module

TL;DR

DeepStreet is a novel deep learning approach that automatically generates urban street networks by learning from existing patterns, aiding urban design and planning especially in rapidly urbanizing areas.

Contribution

The paper introduces DeepStreet, a CNN-based method for predicting and generating urban street networks conditioned on local features, demonstrating its effectiveness in complex city layouts.

Findings

Successfully detects and clusters different street patterns in Barcelona.

Predicts both grid-like and irregular street networks.

Shows potential as a tool for urban design and planning.

Abstract

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.