Survey of City-Wide Homelessness Detection Through Environmental Sensing
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
The growing homelessness crisis in the U.S. presents complex social,
economic, and public health challenges, straining shelters, healthcare, and
social services while limiting effective interventions. Traditional assessment
methods struggle to capture its dynamic, dispersed nature, highlighting the
need for scalable, data-driven detection. This survey explores computational
approaches across four domains: (1) computer vision and deep learning to
identify encampments and urban indicators of homelessness, (2) air quality
sensing via fixed, mobile, and crowdsourced deployments to assess environmental
risks, (3) IoT and edge computing for real-time urban monitoring, and (4)
pedestrian behavior analysis to understand mobility patterns and interactions.
Despite advancements, challenges persist in computational constraints, data
privacy, accurate environmental measurement, and adaptability. This survey
synthesizes recent research, identifies key gaps, and highlights opportunities
to enhance homelessness detection, optimize resource allocation, and improve
urban planning and social support systems for equitable aid distribution and
better neighborhood conditions.
