| id |
caadria2022_317 |
| authors |
Grugni, Francesco, Voltolina, Marco and Cattaneo, Tiziano |
| year |
2022 |
| title |
Use of Object Recognition AI in Community and Heritage Mapping for the Drafting of Sustainable Development Strategies Suitable for Individual Communities, With Case Studies in China, Albania and Italy |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.717
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 717-726 |
| summary |
In order to plan effective strategies for the sustainable development of individual communities, as prescribed by the United Nations‚ Sustainable Development Goal 11, it is necessary for designers and policy makers to gain a deep awareness of the bond that connects people to their territory. AI-driven technologies, and specifically Object Recognition algorithms, are powerful tools that can be used to this end, as they make it possible to analyse huge amounts of pictures shared on social media by residents and visitors of a specific area. A model of the emotional, subjective point of view of the members of the community is thus generated, giving new insights that can support traditional techniques such as surveys and interviews. For the purposes of this research, three case studies have been considered: the neighbourhood around Siping Road in Shanghai, China; the village of Moscopole in southeastern Albania; the rural area of Oltrep Pavese in northern Italy. The results demonstrate that a conscious use of AI-driven technologies does not necessarily imply homogenisation and flattening of individual differences: on the contrary, in all three cases diversities tend to emerge, making it possible to recognise and enhance the individuality of each community and the genius loci of each place. |
| keywords |
sustainable communities, artificial intelligence, object recognition, social media, SDG 11 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (932,507 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/07/22 07:34 |
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