RECOMM. Measuring Resilient Communities: an analytical and predictive tool

Silvio Carta; Tommaso Turchi; Luigi Pintacuda; Ljubomir Jankovic

RECOMM. Measuring Resilient Communities: an analytical and predictive tool

Silvio Carta, Tommaso Turchi, Luigi Pintacuda, Ljubomir Jankovic

Research output: Contribution to journal › Article › peer-review

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 (CNNs) 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
Journal	International Journal of Architectural Computing
Publication status	Accepted/In press - 28 Mar 2023

Keywords

Sustainable Cities and Communities
Resilient Communitie
CNN
urban morphology

Cite this

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title = "RECOMM. Measuring Resilient Communities: an analytical and predictive tool",

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 (CNNs) 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.",

keywords = "Sustainable Cities and Communities, Resilient Communitie, CNN, urban morphology",

author = "Silvio Carta and Tommaso Turchi and Luigi Pintacuda and Ljubomir Jankovic",

year = "2023",

month = mar,

day = "28",

language = "English",

journal = "International Journal of Architectural Computing",

publisher = "SAGE Publications",

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T1 - RECOMM. Measuring Resilient Communities: an analytical and predictive tool

AU - Carta, Silvio

AU - Turchi, Tommaso

AU - Pintacuda, Luigi

AU - Jankovic, Ljubomir

PY - 2023/3/28

Y1 - 2023/3/28

N2 - 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 (CNNs) 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.

AB - 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 (CNNs) 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.

KW - Sustainable Cities and Communities

KW - Resilient Communitie

KW - CNN

KW - urban morphology

M3 - Article

JO - International Journal of Architectural Computing

JF - International Journal of Architectural Computing

ER -

RECOMM. Measuring Resilient Communities: an analytical and predictive tool

Abstract

Keywords

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