Development of a Perception-Based Urban Quality of Life Index Using Street View Imagery and Deep Learning: The Case of Metro Manila, Philippines

Abstract. Urban quality of life (QoL) assessments often rely on objective spatial indicators such as infrastructure access, land use, and environmental conditions. However, these metrics may overlook how residents subjectively perceive their surroundings. This disconnect signifies a methodological deficiency within urban studies: the lack of inclusive frameworks that integrate both objective and perceptual aspects of urban quality. In response, this study introduces a perception-based urban quality of life index (PUQLI) derived from street view imagery and deep learning and compares it against a composite objective indicator built from 13 spatially measured indicators across seven QoL domains. Each indicator was normalized and spatially joined to a hexagonal grid system. Pearson correlation revealed only modest associations between PUQLI and individual objective indicators, suggesting partial alignment. A mismatch was computed to quantify perception–provision gaps, revealing statistically significant and spatially patterned divergences (t = –10.535, p < 0.0001). Areas of under-perception and over-perception were examined, which provide critical spatial insights for the formulation of planning interventions. These findings underscore the necessity of integrating subjective perceptions into urban assessment frameworks to ensure that the provision of infrastructure effectively translates into tangible enhancements in urban quality. The mismatch index serves as a pragmatic diagnostic tool for perception-informed urban development.