Reducing the social inequity of neighborhood visual environment in Los Angeles through computer vision and multi-model machine learning.

ABSTRACT: Aligning with the United Nations' Sustainable Development Goals, the focus on creating safe, sustainable cities and enhancing the wellbeing of individuals across all age groups has become a central aspect of urban planning and environmental management. The environments we live in significantly influence our thoughts, emotions, and interactions with the world around us. Our study aims to unveil the social inequity of citizens' wellbeing, reflected by their perception on neighborhood environment (e.g., feeling of depression), across different social/vulnerable groups (i.e., White, Black, Asian, Hispanic, low-income, low-educated, and unemployed) via crowdsourced street view imageries and computer vision. Specifically, we quantified the actual built environment in the 5D dimensions (i.e., den

SUBMITTER: Wang S 

PROVIDER: S-EPMC11781154 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature