Abascal, Angela, Vanhuysse, Sabine, Grippa, Taïs, Rodriguez-Carreño, Ignacio, Georganos, Stefanos, Wang, Jiong, Kuffer, Monika, Martinez-Diez, Pablo, Santamaria-Varas, Mar, Wolff, Eleonore, Abascal, Angela, Vanhuysse, Sabine, Grippa, Taïs, Rodriguez-Carreño, Ignacio, Georganos, Stefanos, Wang, Jiong, Kuffer, Monika, Martinez-Diez, Pablo, Santamaria-Varas, Mar, and Wolff, Eleonore
Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted the potential of Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects of urban deprivation. However, little research has explored AI’s ability to predict how locals perceive deprivation. This research aims to develop a method to predict citizens’ perception of deprivation using satellite imagery, citizen science, and AI. A deprivation perception score was computed from slum-citizens’ votes. Then, AI was used to model this score, and results indicate that it can effectively predict perception, with deep learning outperforming conventional machine learning. By leveraging AI and EO, policymakers can comprehend the underlying patterns of urban deprivation, enabling targeted interventions based on citizens’ needs. As over a quarter of the global urban population resides in slums, this tool can help prioritise citizens’ requirements, providing evidence for implementing urban upgrading policies aligned with SDG-11.