Milton, R; Roumpani, F; (2019) Accelerating Urban Modelling Algorithms with Artificial Intelligence. In: Grueau, C and Laurini, R and Ragia, L, (eds.) Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM. (pp. pp. 105-116). INSTICC: Heraklion, Crete, Greece.

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Abstract

In this paper, we demonstrate that developments in computer hardware to support the increasingly complex artificial intelligence workflows for Deep Learning networks can be adapted for urban modelling and visualisation. The hypothesis here is that by leveraging the current practice of AI as a Service (AIaaS), then this enables Urban Modelling as a Service (UMaaS) to be developed. The starting point for this paper is a 3D visualisation of the Queen Elizabeth Olympic Park, developed using a web-based spatial interaction modelling system which calculates population metrics on the fly, capable of showing the results of interventions by urban planners in real-time. We take the web application that powers the interactive visualisation and use Google’s TensorFlow AI library to accelerate the matrix operations required to run the spatial interaction model, making the web application fast enough to be used interactively.

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