Minimally supervised learning on sub-meter satellite imagery reveals slum expansion during the COVID-19 pandemic

Abstract Slums, or deprived urban settlements, are characterized by inadequate housing, limited access to essential services, and overcrowding. Therefore, monitoring the populations living in these areas is crucial for promoting sustainable urbanization and implementing effective, targeted policy interventions. Deep learning-based remote sensing technologies applied at sub-meter resolution can detect informal settlements with high accuracy. However, scaling to multi-temporal and cross-national contexts remains challenging. Here, we developed a scalable computer vision model that detects slums across diverse geographies and timescales, using 60-cm-resolution images and minimal labeled data. We applied the model to 12 cities in low- and middle-income countries from 2014 to 2024. Our longitudinal analysis indicates that informal settlements expanded during the COVID-19 pandemic, even as global estimates from UN-Habitat suggest an overall long-term decline in slum prevalence. Our results demonstrate that redevelopment programs intended to improve slum conditions are associated with unintended spatial spillovers, including new settlement growth in surrounding areas.