Survey of City-Wide Homelessness Detection Through Environmental Sensing

TL;DR

The paper surveys sensing-based approaches to detecting homelessness, framing four domains—computer vision for encampment detection, air-quality sensing for environmental risk, IoT/edge computing for distributed monitoring, and pedestrian-behavior analytics for mobility insights. It synthesizes methods from satellite and mobile imagery, fixed and mobile pollution sensors, participatory sensing, and real-time edge processing to provide scalable, data-driven insights. Key contributions include identifying gaps in data quality, privacy, computational constraints, and adaptability, and outlining opportunities to improve resource allocation, urban planning, and equitable aid delivery. The work highlights the practical impact of integrating multi-modal sensing to support public health, safety, and neighborhood improvements while stressing the need for responsible data use and cross-sector collaboration.

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.