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Authors: Richard Milton 1 and Flora Roumpani 2

Affiliations: 1 Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, WC1E 6BT and U.K. ; 2 The Alan Turing Institute, The British Library, London and U.K.

Keyword(s): Urban Modelling, Spatial Interaction Modelling, Artificial Intelligence, 3D Visualisation.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Communication Networking ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Pattern Recognition ; Performance Evaluation ; Software Engineering ; Software Project Management ; Symbolic Systems ; Telecommunications ; Web Applications

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Milton, R. and Roumpani, F. (2019). Accelerating Urban Modelling Algorithms with Artificial Intelligence. In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-371-1; ISSN 2184-500X, SciTePress, pages 105-116. DOI: 10.5220/0007727201050116

@conference{gistam19,
author={Richard Milton. and Flora Roumpani.},
title={Accelerating Urban Modelling Algorithms with Artificial Intelligence},
booktitle={Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2019},
pages={105-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007727201050116},
isbn={978-989-758-371-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Accelerating Urban Modelling Algorithms with Artificial Intelligence
SN - 978-989-758-371-1
IS - 2184-500X
AU - Milton, R.
AU - Roumpani, F.
PY - 2019
SP - 105
EP - 116
DO - 10.5220/0007727201050116
PB - SciTePress