| id |
ecaade2024_253 |
| authors |
Ke, Daijia; Agniputra, Akbar; Feng, Zhaoyan; Wu, Ilin; Di Carlo, Ilaria; Papeschi, Annarita |
| year |
2024 |
| title |
Cartographies of Immersive Fractality: An exploration of collective emotive responses in urban settings through Machine Learning |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.519
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 519–528 |
| summary |
Recent advances in machine learning technologies offer avenues for a more efficient analysis of large photographic and text-based datasets, facilitating a deeper understanding of the fundamental characteristics inherent in the immersive representation of the urban environment. It is known that automatic fractal processing in the human visual system triggers positive emotive responses to the environment. The project explores the correlation among fractal aesthetics, visual perception, and emotional responses in urban settings, developing an integrated evaluation method that uses the data-scraping of existing online photographic media from Flickr and Google Street View (GSV). Taking the area of Southbank in London (UK) as a case study, the study initially employed a sentiment analysis method rooted in the Lexical dictionary from TextBlob. Further, an extensive online GSV urban scenery dataset was built via Google API. The photographic dataset was then evaluated by fractal dimension as a quantitative index to measure the complexity of fractal patterns. Concurrently, to enhance the comprehension of the composition of urban form, a semantic segmentation method for image analysis was implemented. A comparative evaluation of the data collected indicated the key role of fractal patterns described by vegetation in the generation of positive emotional responses, underscoring with methodological rigour the potentially transformative impact of the experience of fractal patterns and green infrastructures in open urban spaces. |
| keywords |
Visual perception, Sentiment analysis, Psychogeography, Fractal aesthetics, Machine Learning |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,074,626 bytes) |
| references |
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| last changed |
2024/11/17 22:05 |
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