Research on Urban Renewal Public Space Design Based on Convolutional Neural Network Model

Abstract

By establishing a database of urban space cases, machine learning algorithms and deep learning algorithms can be used to train computers to learn how to design urban spaces. Based on the basic concepts of machine learning and deep learning and their procedural logic, this paper explores the generation mode of traffic road network, neighborhood space form, and building function layout of urban space and uses the northern extension of the central green axis of the city as an application case to confirm its feasibility in order to seek a set of artificial intelligence-based urban space generation design method and provide a new idea for the innovative development of urban design methods.