Image Segmentation and Emotional Analysis of Virtual and Augmented Reality Urban Scenes

This paper explores how image segmentation, virtual reality, and augmented reality can be combined to evaluate citizens’ emotional responses to urban environments during participatory planning processes. The research was conducted in the Porta Romana district of Milan, an area undergoing major transformation from former industrial and railway uses into a new business-oriented neighborhood. Two complementary studies were developed: the first used VR panoramic Street View scenes shown indoors to students, while the second employed outdoor AR visualization of the proposed VITAE redevelopment project through a mobile application during a public event. Participants assessed their emotional reactions through the experiential Environmental Impact Assessment (exp-EIA) method, based on Russell’s circumplex model of emotions. At the same time, semantic image segmentation algorithms quantified the visible proportion of urban elements such as trees, buildings, roads, walls, and pavements. Results show stable correlations across both studies: trees reduce unpleasantness and emotional arousal, while buildings tend to increase unpleasantness and paved surfaces increase arousal. The study demonstrates that affordable immersive tools and AI-based scene analysis can support evidence-based urban design, citizen participation, and healthier planning strategies.