Publications
CONFERENCE (INTERNATIONAL) Omni-CityMood: Vision-based Urban Atmosphere Perception from Every Angle
Yuki Kubota (Institute of Science Tokyo), Kota Tsubouchi, Soto Anno (Institute of Science Tokyo), Kaito Ide (Institute of Science Tokyo), Masamichi Shimosaka (Institute of Science Tokyo)
The 33rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2025)
December 22, 2025
Understanding how cities are perceived from on-site visitors’ perspectives can provide valuable insights for urban planning and development applications. However, existing studies estimated people’s perceptions by having them view photographed landscape images; the scores derived by these methods were thus merely quantified impressions of specific viewpoints that do not necessarily represent perceptions people would have were they at the site. To address this issue, we developed a framework, named Omni-CityMood, for quantifying people’s on-site perceptions of urban atmospheres. Based on the idea that the viewpoint influences the perception of an urban landscape, the proposed framework identifies critical viewpoints of a location by using both visual-based features of landscape images and geographical characteristics of the site. In particular, Omni-CityMood enables the mood of a location to be evaluated from viewpoints over a range of 360 degrees by leveraging the techniques of neural recommendation systems. We evaluated Omni-CityMood on a dataset we built that includes perceived atmosphere experiences in various cities. Experiments and extensive analyses demonstrate the promising capability of modeling landscape viewpoints to quantify urban on-site atmospheres.
Paper :
Omni-CityMood: Vision-based Urban Atmosphere Perception from Every Angle
(external link)