A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban Forestry Science
The project called urban forestry science was developed in December 2020, within the wider framework of the GreeMta competition, where it was selected as the winning proposal. Trees represent a fundamental economic, social, and environmental asset for our urban areas. The green canopy infrastructure provides multifaceted benefits for the planet and citizens, whether in parks or along streets. Analysing and predicting the optimal locations for trees at a granular scale are key factors for quantifying the benefits in terms of both environmental benefit and return on investment. In conclusion, knowing the optimal species and locations for green canopy, based on key performance indicators, allows us to better estimate and predict trees' impact within the proximal surrounding area. The final achievement of the artificial neural network was to detect, with high accuracy, tree canopy infrastructure, thus minutely refining the city council inventory.
Urban Forestry Science
The project called urban forestry science was developed in December 2020, within the wider framework of the GreeMta competition, where it was selected as the winning proposal. Trees represent a fundamental economic, social, and environmental asset for our urban areas. The green canopy infrastructure provides multifaceted benefits for the planet and citizens, whether in parks or along streets. Analysing and predicting the optimal locations for trees at a granular scale are key factors for quantifying the benefits in terms of both environmental benefit and return on investment. In conclusion, knowing the optimal species and locations for green canopy, based on key performance indicators, allows us to better estimate and predict trees' impact within the proximal surrounding area. The final achievement of the artificial neural network was to detect, with high accuracy, tree canopy infrastructure, thus minutely refining the city council inventory.
Urban Forestry Science
Carta, Silvio (editor) / Testi, Iacopo (author)
Machine Learning and the City ; 517-520
2022-05-21
4 pages
Article/Chapter (Book)
Electronic Resource
English
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