User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe

The rapid urbanization in many regions worldwide results in the proliferation of deprived urban areas, also known as slums or informal settlements. Our study addresses the pressing need for accurate information by investigating User and Data-centric Artificial Intelligence (AI)-based methods for mapping deprived urban areas and extracting information supporting the Sustainable Development Goals (SDG) Indicator 11.1.1. In collaboration with local communities and several (inter)national stakeholders, we co-designed AI strategies based on free or low-cost Earth Observation (EO) and geospatial data to map informal settlements in eight cities across the globe. The AI methods design, data collection, and validation strategies follow an iterative and agile process consisting of progressive refinement stages necessary to collect reliable labeled data and take user requirements into their centre. Our findings indicate that the combination of Sentinel-2 and morphometric features yields the most accurate results.