TY - JOUR
T1 - AI perceives like a local
T2 - predicting citizen deprivation perception using satellite imagery
AU - Abascal, Angela
AU - Vanhuysse, Sabine
AU - Grippa, Taïs
AU - Rodriguez-Carreño, Ignacio
AU - Georganos, Stefanos
AU - Wang, Jiong
AU - Kuffer, Monika
AU - Martinez-Diez, Pablo
AU - Santamaria-Varas, Mar
AU - Wolff, Eleonore
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - ITC-GOLD
U2 - 10.1038/s42949-024-00156-x
DO - 10.1038/s42949-024-00156-x
M3 - Article
AN - SCOPUS:85188964030
SN - 2661-8001
VL - 4
SP - 1
EP - 14
JO - npj Urban Sustainability
JF - npj Urban Sustainability
IS - 1
M1 - 20
ER -