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Zapata"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"https://ror.org/03y3y9v44","expandedvalue":{"scheme":"http://www.grid.ac/ontology/","termName":"Universidad EAFIT","@type":"https://schema.org/Organization"}}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Bedru Tereke"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Faculty of Geo-Information Science and Earth Observation, University of Twente"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"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.\n<br>\nWorking 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.\n<br>\nAs 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. 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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."}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Earth and Environmental Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"urban deprivation, informal settlements, earth observation, deep learning"}}]},{"typeName":"language","multiple":true,"typeClass":"controlledVocabulary","value":["English"]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2024-11-13"},{"typeName":"productionPlace","multiple":true,"typeClass":"primitive","value":["Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, 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