Varoudis, T; Penn, A; (2019) Variational Beauty of Space: Machine Intuition and Non-Linear Neural Aggregations. In: Duan, Jin, (ed.) Proceedings of the 12th International Space Syntax Symposium (12SSS). 12th International Space Syntax Symposium (12SSS): Beijing,China.

Preview

Text SSS12_2018_VAROUDIS_PENN_Variational Beauty of Space_Final.pdf - Published Version Download (4MB) | Preview

Abstract

Spatial networks have long been known for their internal spatial order or beauty (Hillier, 2007) and in the field spatial computation and analytics we employ a number of graph based methodologies in order to understand or extract intrinsic attributes of the urban fabric around us. In this research we take a complete different approach by looking at urban structure through the use of deep convolutional variational autoencoders with interesting results. // Autoencoders are an unsupervised learning technique in which we employ neural networks for the task of representation learning. Specifically, a neural network architecture imposes a bottleneck in the network which forces a compressed and generalization of the knowledge representation of the structure of space. This non-linear compression and subsequent reconstruction creates a unique set of features that are inherent of urban space. Our network is multiple layers deep in order to be able to encode basic spatial complexity and is build based on a convolutional network architecture which is inspired by biological processes similar to the connectivity pattern between neurons that resembles the organization of the animal visual cortex. Artificial neurons respond to real urban networks of London in a restricted region of the visual field, which partially overlap such that they cover the entire convolutional visual field. Convolutional layers apply a convolution operation to the input, passing the result to the next layer. Each convolutional neuron processes data only for its receptive field but the cascading nature of our network build a knowledge of more complex spatial relations as data move deeper. While convolutional neural networks are extensively used in supervised image classification the work presented in this papers is completely unsupervised. // In this paper we present the complete architecture of the deep convolutional network that was trained for months using data from the city of London, concluding with two significant outcomes extracted from the variational encoding and decoding processes. A non-linear clustering, or neural aggregation, of urban space based on the learned features and a generative urban network output based on the variational synthesis we call machine intuition.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item