Cumincad : CUMINCAD Papers : Paper cdrf2022_274:Nolli Map: Interpretation of Urban Morphology Based on Machine Learning

id	cdrf2022_274
authors	Zhiyong Dong and Jinru Lin
year	2022
title	Nolli Map: Interpretation of Urban Morphology Based on Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_24
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Nolli map is the earliest diagram tool to simplify and quantify urban form, which most intuitively reflects the spatial layout of tangible elements in the city. The urban morphology contains its inherent evolutionary laws. Exploring the inner rules of cities is helpful for people to conduct urban research and design. Unlike the traditional research methods of urban morphology, the neural network algorithm provides us with new ideas for understanding urban morphology. In this experiment, we label 136 European cities samples in the rules of Nolli map as a training set for machine learning. We use Generative Adversarial Networks (GAN) for multiple mapping experiments. The generated images present recognizable and plausible images of the urban fabric. The results show that the machine can learn the inherent laws of complex urban fabrics, which expands a new applied method for the study of urban morphology.
series	cdrf
email
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