Author:
Canepa Maria,Magliocco Adriano,Pisani Nicola
Abstract
AbstractTo address Sustainable Development Goals (SDGs) and face climate change effects, it is necessary to adopt multidisciplinary methodologies and strategies for risk prevention and mitigation of the impact in urban contexts. These phenomena represent a risk for cultural heritage conservation, with negative consequences for local economies. To move from the analysis of climate impacts to adaptation measures and governance tools, it is necessary to deal with the different characteristics of the urban context in its physical, historical, cultural, and socio-economic components. The paper focuses on the collaboration between UNIGE Architecture and Design Department (DAD), and Colouree S.r.l. that has developed an analytical platform that uses artificial intelligence, geo-referenced data, and automated analysis to define the characteristics of the urban context. The aim of the research is the identification of parameters and solutions to respond to the effects of climate change in the urban environment, considering risk levels and context settlement; alongside the climatic skills, also the architects’ skills in environmental technologies, urban landscape, and cultural heritage have been given relevance. DAD aims to capitalize on the previous and ongoing experiences of Colouree, offering scientific and methodological support, to reach the definition of a detailed settlement analysis, providing indications on the risks associated with the main predictable effects (extreme weather events, heat island effect, water availability). The expected results will define a methodological structure to create a sensitivity mapping to meteorological phenomena, based on the data support from Colouree towards the carrying capacity of the urban fabric, making information more accessible thanks to the data visualization and web-based mapping, including, among the stakeholders, not only experts but also professionals and citizens.
Publisher
Springer International Publishing
Cited by
1 articles.
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1. Climate change visualization awareness system;i-manager's Journal on Computer Science;2024