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RECOMM. Measuring Resilient Communities: an analytical and predictive tool

RECOMM. Measuring Resilient Communities: an analytical and predictive tool

Carta, Silvio ORCID: 0000-0002-7586-3121, Turchi, Tommaso, Pintacuda, Luigi and Jankovic, Ljubomir (2023) RECOMM. Measuring Resilient Communities: an analytical and predictive tool. International Journal of Architectural Computing, 21 (3). pp. 536-560. ISSN 1478-0771 (Print), 2048-3988 (Online) (doi:

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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.

Item Type: Article
Additional Information: Special Issue: CAADRIA 2023.
Uncontrolled Keywords: sustainable cities and communities; Resilient Communities; CNN; urban morphology
Subjects: H Social Sciences > H Social Sciences (General)
N Fine Arts > NA Architecture
Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Design (DES)
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Last Modified: 18 Oct 2023 07:44

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