The rapid urbanization across many regions worldwide has significantly increased the spread of deprived urban areas, often called slums or informal settlements. The lack of reliable geospatial information on their extent and locations in many cities continues to hinder efforts aimed at improving living conditions. This research addresses this critical information gap by exploring a User- and Data-centric Artificial Intelligence (AI) approach for accurately mapping these areas to support Sustainable Development Goal (SDG) Indicator 11.1.1.<br>Working closely with local communities and a range of (inter)national stakeholders, we co-designed an AI-driven strategy utilizing open Earth Observation (EO) and geospatial data to map deprived settlements in eight cities worldwide. Our approach integrates an iterative, agile process for AI model design, data collection, and validation, incorporating progressive refinement stages to ensure high-quality labelled data and centralize user needs. A collaborative data collection platform (https://portal.ideatlas.eu/ ) was created to support community involvement and improve data quality.<br>As a key outcome of this research, we are excited to announce the release of IDEABench , a groundbreaking benchmark dataset that combines multiple sources of data to help researchers improve their understanding of deprived urban areas. This benchmark dataset contains image patches from two satellite systems, Sentinel-1 and Sentinel-2, along with precomputed buildup density information. Furthermore, it includes detailed annotations for three distinct categories: deprived urban areas, non-deprived urban areas, and non-built-up areas. The dataset comprises a total of 47,476 patches , each with a size of 128×128 pixels from 8 cities across the globe, including Nairobi, Medellín, Mumbai, Buenos Aires, Lagos, Jakarta, Mexico City, and Salvador.<br>IDEABench has the potential to support a wide range of applications, from identifying areas of deprivation and tracking urban growth to informing policy decisions and promoting more equitable urban development. We believe that this dataset will be a valuable resource for the research community and look forward to seeing the innovative solutions it will enable.<br>The IDEABench dataset is an outcome of the IDEAtlas project, a research initiative funded by the European Space Agency (ESA). The project is led by the Faculty of Geo-Information Science and Earth Observation at the University of Twente in partnership with GeoVille Information Systems and Data Processing GmbH.