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 languageEnglish
Pages (from-to)536-560
Number of pages25
JournalInternational Journal of Architectural Computing
Volume21
Issue number3
Early online date29 Jul 2023
DOIs
Publication statusPublished - 30 Sept 2023

Keywords

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

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