How People Recognize a Street: Enhancing Perceived Identity for Socio-Environmental Sustainability

Recognizable and distinctive streets are essential not only for navigation but also for fostering place identity and therefore socio-environmental sustainability in cities. The recognition depends on both high-level visual features (e.g., buildings, trees, etc.) and low-level ones (e.g., colors, spatial frequencies, etc.). While the former has been examined extensively, the latter remains less understood. This study addresses this gap via a multi-disciplinary perspective, by exploring how low-level visual features influence attention and cognitive processing during street recognition using an eye-tracking device. In the experiment, participants were expected to recognize Huaihai Road in China from other historic, tree-shaded, commercial streets with similar appearance (e.g., the Champs-Élysées in France, Omotesando in Japan, and Shaanxi South Road and Fuxing Middle Road in China). Results showed that removing mid-to-high spatial frequencies significantly improved recognition accuracy, while the absence of color led to a notable decline in accuracy. Markedly, the presence or absence of trees did not significantly affect recognition accuracy, suggesting that trees may be not vital for street recognition. These findings underscore the importance of global visual cues and color in the recognition process and provide practical computational design insights for urban distinctiveness and cultural sustainability.