User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities

Rapid urbanization across many regions worldwide has significantly contributed to the growth of deprived urban areas (DUAs), often called slums or informal settlements. The lack of reliable geospatial information on their location and extent in many cities continues to hinder efforts aimed at improv...

Descripción completa

Detalles Bibliográficos
Autores: Tareke, Bedru, Silva Filho, Paulo, Persello, Claudio, Kuffer, Monika, Maretto, Raian V., Wang, Jiong, Abascal, Ángela, Pillai, Priam, Singh, Binti, D'Attoli, Juan Manuel, Kabaria, Caroline, Pedrassoli, Julio, Brito, Patricia, Elias, Peter, Atenógenes, Elio, Ramírez Santiago, Andrea, Pratomo, Jati, Mulyana, Wahyu, Paganini, Marc, Thomson, Dana R., Mwaniki, Dennis, Ospina, Juan P.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:dnet:academicae__::f662c7fba9044754587afd93c644300a
Acceso en línea:https://hdl.handle.net/2454/56891
Access Level:acceso abierto
Palabra clave:Urban deprivation
Informal settlements
Slums
Earth observation
Deep learning
Data-centric AI
id ES_a31ff56a07aaee3f98bcfde2a35cf177
oai_identifier_str oai:dnet:academicae__::f662c7fba9044754587afd93c644300a
network_acronym_str ES
network_name_str España
repository_id_str
spelling User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of citiesTareke, BedruSilva Filho, PauloPersello, ClaudioKuffer, MonikaMaretto, Raian V.Wang, JiongAbascal, ÁngelaPillai, PriamSingh, BintiD'Attoli, Juan ManuelKabaria, CarolinePedrassoli, JulioBrito, PatriciaElias, PeterAtenógenes, ElioRamírez Santiago, AndreaPratomo, JatiMulyana, WahyuPaganini, MarcThomson, Dana R.Mwaniki, DennisOspina, Juan P.Urban deprivationInformal settlementsSlumsEarth observationDeep learningData-centric AIRapid urbanization across many regions worldwide has significantly contributed to the growth of deprived urban areas (DUAs), often called slums or informal settlements. The lack of reliable geospatial information on their location and extent in many cities continues to hinder efforts aimed at improving living conditions. This study 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. In collaboration with local communities, governments, and international stakeholders, we co-designed an AI-driven strategy leveraging open Earth Observation (EO) and geospatial data acrosseight cities worldwide. Instead of relying solely on algorithmic precision, our method prioritizes local knowledge, iterative validation, and adaptive data collection. To achieve this, we developed a tailored multi-branch encoder-decoder convolutional neural network capable of integrating multi-modal data sources. Our approach incorporates an agile and iterativemodel refinement process, ensuring continuous feedback loops between AI design, data collection, and validation. Recognizing the importance of stakeholder engagement, we developed the IDEAtlas collaborative data collection platform - https://portal.ideatlas.eu/ - to enhance data quality and inclusivity. The resulting dataset (IDEABench) is publicly available at https://doi.org/10.17026/PT/X4NJII to facilitate continued research and development. Findings indicate that fusing multi-spectral EO data with urban morphometric features, particularly Sentinel-2 imagery and built-up density, provides the highest accuracy for identifying DUAs. Furthermore, improvements in reference data quality through the IDEAtlas platform led to increased mapping precision. However, the significant variability in accuracy across cities underscores the complexity of the task and suggests the need for supplementary geospatial data to complement EO-driven analysis. The code used in this study is available at https://github.com/IDEAtlas/ai-dua-mapping.This work was supported by the European Space Agency through the IDEAtlas Project [Contract-No. 4000139510/22/I-DT].ElsevierIngenieríaIngeniaritza2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/56891reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2026 The Author(s). Published by Elsevier Inc.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:academicae__::f662c7fba9044754587afd93c644300a2026-06-17T12:41:47Z
dc.title.none.fl_str_mv User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
title User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
spellingShingle User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
Tareke, Bedru
Urban deprivation
Informal settlements
Slums
Earth observation
Deep learning
Data-centric AI
title_short User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
title_full User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
title_fullStr User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
title_full_unstemmed User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
title_sort User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
dc.creator.none.fl_str_mv Tareke, Bedru
Silva Filho, Paulo
Persello, Claudio
Kuffer, Monika
Maretto, Raian V.
Wang, Jiong
Abascal, Ángela
Pillai, Priam
Singh, Binti
D'Attoli, Juan Manuel
Kabaria, Caroline
Pedrassoli, Julio
Brito, Patricia
Elias, Peter
Atenógenes, Elio
Ramírez Santiago, Andrea
Pratomo, Jati
Mulyana, Wahyu
Paganini, Marc
Thomson, Dana R.
Mwaniki, Dennis
Ospina, Juan P.
author Tareke, Bedru
author_facet Tareke, Bedru
Silva Filho, Paulo
Persello, Claudio
Kuffer, Monika
Maretto, Raian V.
Wang, Jiong
Abascal, Ángela
Pillai, Priam
Singh, Binti
D'Attoli, Juan Manuel
Kabaria, Caroline
Pedrassoli, Julio
Brito, Patricia
Elias, Peter
Atenógenes, Elio
Ramírez Santiago, Andrea
Pratomo, Jati
Mulyana, Wahyu
Paganini, Marc
Thomson, Dana R.
Mwaniki, Dennis
Ospina, Juan P.
author_role author
author2 Silva Filho, Paulo
Persello, Claudio
Kuffer, Monika
Maretto, Raian V.
Wang, Jiong
Abascal, Ángela
Pillai, Priam
Singh, Binti
D'Attoli, Juan Manuel
Kabaria, Caroline
Pedrassoli, Julio
Brito, Patricia
Elias, Peter
Atenógenes, Elio
Ramírez Santiago, Andrea
Pratomo, Jati
Mulyana, Wahyu
Paganini, Marc
Thomson, Dana R.
Mwaniki, Dennis
Ospina, Juan P.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
dc.subject.none.fl_str_mv Urban deprivation
Informal settlements
Slums
Earth observation
Deep learning
Data-centric AI
topic Urban deprivation
Informal settlements
Slums
Earth observation
Deep learning
Data-centric AI
description Rapid urbanization across many regions worldwide has significantly contributed to the growth of deprived urban areas (DUAs), often called slums or informal settlements. The lack of reliable geospatial information on their location and extent in many cities continues to hinder efforts aimed at improving living conditions. This study 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. In collaboration with local communities, governments, and international stakeholders, we co-designed an AI-driven strategy leveraging open Earth Observation (EO) and geospatial data acrosseight cities worldwide. Instead of relying solely on algorithmic precision, our method prioritizes local knowledge, iterative validation, and adaptive data collection. To achieve this, we developed a tailored multi-branch encoder-decoder convolutional neural network capable of integrating multi-modal data sources. Our approach incorporates an agile and iterativemodel refinement process, ensuring continuous feedback loops between AI design, data collection, and validation. Recognizing the importance of stakeholder engagement, we developed the IDEAtlas collaborative data collection platform - https://portal.ideatlas.eu/ - to enhance data quality and inclusivity. The resulting dataset (IDEABench) is publicly available at https://doi.org/10.17026/PT/X4NJII to facilitate continued research and development. Findings indicate that fusing multi-spectral EO data with urban morphometric features, particularly Sentinel-2 imagery and built-up density, provides the highest accuracy for identifying DUAs. Furthermore, improvements in reference data quality through the IDEAtlas platform led to increased mapping precision. However, the significant variability in accuracy across cities underscores the complexity of the task and suggests the need for supplementary geospatial data to complement EO-driven analysis. The code used in this study is available at https://github.com/IDEAtlas/ai-dua-mapping.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/56891
url https://hdl.handle.net/2454/56891
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv © 2026 The Author(s). Published by Elsevier Inc.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2026 The Author(s). Published by Elsevier Inc.
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415354569064448
score 15,811543

Similares en LA Referencia