RECOMM. Measuring resilient communities: An analytical and predictive tool

Silvio Carta; Tommaso Turchi; Luigi Pintacuda; Ljubomir Jankovic

doi:10.1177/14780771231174891

RECOMM. Measuring resilient communities: An analytical and predictive tool

Silvio Carta, Tommaso Turchi, Luigi Pintacuda, Ljubomir Jankovic

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Abstract

We present initial findings of our project RECOMM: an analytical tool that evaluates the resilience of urban areas. The tool utilises Deep Neural Networks to identify characteristics of resilience and assigns a resilience score to different urban areas based on the proximity to certain features such as green spaces, buildings, natural elements and infrastructure. The tool also identifies which urban morphological factors have the greatest impact on resilience. The method uses Convolutional Neural Networks with the Keras library on Tensorflow for calculations and the results are displayed in an online demo built with Node.js and React.js. This work contributes to the analysis and design of sustainable cities and communities by offering a tool to assess resilience through urban form.

Original language	English
Pages (from-to)	536-560
Number of pages	25
Journal	International Journal of Architectural Computing
Volume	21
Issue number	3
Early online date	29 Jul 2023
DOIs	https://doi.org/10.1177/14780771231174891
Publication status	Published - 30 Sept 2023

Keywords

Sustainable Cities and Communities
Resilient Communitie
CNN
urban morphology
resilient communities
Sustainable cities and communities

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10.1177/14780771231174891Licence: CC BY

Final_Version
© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
Final published version, 3.27 MBLicence: CC BY

Cite this

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RECOMM. Measuring resilient communities: An analytical and predictive tool

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