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

Bedru Tareke; Paulo Silva Filho; Claudio Persello; Monika Kuffer; Raian Maretto; Jon Wang; Angela Abascal; Priam Pillai; Binti Singh; Juan Manuel D’Attoli; Caroline Kabaria; Juilo Pedrassoli; Patricia Brito; Peter Elias; Elio Atenógenes; Andrea Ramírez Santiago

doi:10.1109/IGARSS53475.2024.10640428

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

Bedru Tareke, Paulo Silva Filho, Claudio Persello, Monika Kuffer, Raian Maretto, Jon Wang, Angela Abascal, Priam Pillai, Binti Singh, Juan Manuel D’Attoli, Caroline Kabaria, Juilo Pedrassoli, Patricia Brito, Peter Elias, Elio Atenógenes, Andrea Ramírez Santiago

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	IGARSS 2024
Subtitle of host publication	2024 IEEE International Geoscience and Remote Sensing Symposium
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1553-1557
Number of pages	5
ISBN (Electronic)	9798350360325
ISBN (Print)	979-8-3503-6032-5
DOIs	https://doi.org/10.1109/IGARSS53475.2024.10640428
Publication status	Published - 2024
Event	IEEE International Symposium on Geoscience and Remote Sensing, IGARRS 2024: Acting for sustainability and resilience - Athens, Greece Duration: 7 Jul 2024 → 12 Jul 2024 https://www.2024.ieeeigarss.org/index.php

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	IEEE
Volume	2024
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	IEEE International Symposium on Geoscience and Remote Sensing, IGARRS 2024
Abbreviated title	IGARRS 2024
Country/Territory	Greece
City	Athens
Period	7/07/24 → 12/07/24
Internet address	https://www.2024.ieeeigarss.org/index.php

Keywords

Training
Visualization
Accuracy
Urban areas
Collaboration
Reliability engineering
Data models
Informal Settlement
Deep Learning
Fully Convolutional Network
Urban Morphometrics
Earth Observation
2024 OA procedure

Access to Document

10.1109/IGARSS53475.2024.10640428

Cite this

Tareke, B., Filho, P. S., Persello, C., Kuffer, M., Maretto, R., Wang, J., Abascal, A., Pillai, P., Singh, B., D’Attoli, J. M., Kabaria, C., Pedrassoli, J., Brito, P., Elias, P., Atenógenes, E., & Santiago, A. R. (2024). User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe. In IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 1553-1557). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2024). IEEE. https://doi.org/10.1109/IGARSS53475.2024.10640428

@inproceedings{220dde25737142da84781a92b05f6ac5,

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

abstract = "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.",

keywords = "Training, Visualization, Accuracy, Urban areas, Collaboration, Reliability engineering, Data models, Informal Settlement, Deep Learning, Fully Convolutional Network, Urban Morphometrics, Earth Observation, 2024 OA procedure",

author = "Bedru Tareke and Filho, {Paulo Silva} and Claudio Persello and Monika Kuffer and Raian Maretto and Jon Wang and Angela Abascal and Priam Pillai and Binti Singh and D{\textquoteright}Attoli, {Juan Manuel} and Caroline Kabaria and Juilo Pedrassoli and Patricia Brito and Peter Elias and Elio Aten{\'o}genes and Santiago, {Andrea Ram{\'i}rez}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; IEEE International Symposium on Geoscience and Remote Sensing, IGARRS 2024 : Acting for sustainability and resilience, IGARRS 2024 ; Conference date: 07-07-2024 Through 12-07-2024",

year = "2024",

doi = "10.1109/IGARSS53475.2024.10640428",

language = "English",

isbn = "979-8-3503-6032-5",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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booktitle = "IGARSS 2024",

address = "United States",

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}

Tareke, B, Filho, PS, Persello, C , Kuffer, M , Maretto, R , Wang, J, Abascal, A, Pillai, P, Singh, B, D’Attoli, JM, Kabaria, C, Pedrassoli, J, Brito, P, Elias, P, Atenógenes, E & Santiago, AR 2024, User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe. in IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2024, IEEE, Piscataway, NJ, pp. 1553-1557, IEEE International Symposium on Geoscience and Remote Sensing, IGARRS 2024, Athens, Greece, 7/07/24. https://doi.org/10.1109/IGARSS53475.2024.10640428

User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe. / Tareke, Bedru; Filho, Paulo Silva; Persello, Claudio et al.
IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE, 2024. p. 1553-1557 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe

AU - Tareke, Bedru

AU - Filho, Paulo Silva

AU - Persello, Claudio

AU - Kuffer, Monika

AU - Maretto, Raian

AU - Wang, Jon

AU - Abascal, Angela

AU - Pillai, Priam

AU - Singh, Binti

AU - D’Attoli, Juan Manuel

AU - Kabaria, Caroline

AU - Pedrassoli, Juilo

AU - Brito, Patricia

AU - Elias, Peter

AU - Atenógenes, Elio

AU - Santiago, Andrea Ramírez

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N2 - 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.

AB - 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.

KW - Training

KW - Visualization

KW - Accuracy

KW - Urban areas

KW - Collaboration

KW - Reliability engineering

KW - Data models

KW - Informal Settlement

KW - Deep Learning

KW - Fully Convolutional Network

KW - Urban Morphometrics

KW - Earth Observation

KW - 2024 OA procedure

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U2 - 10.1109/IGARSS53475.2024.10640428

DO - 10.1109/IGARSS53475.2024.10640428

M3 - Conference contribution

SN - 979-8-3503-6032-5

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1553

EP - 1557

BT - IGARSS 2024

PB - IEEE

CY - Piscataway, NJ

T2 - IEEE International Symposium on Geoscience and Remote Sensing, IGARRS 2024

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ER -

Tareke B, Filho PS, Persello C , Kuffer M , Maretto R , Wang J et al. User and data-centric artificial intelligence for mapping urban deprivation in multiple cities across the globe. In IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE. 2024. p. 1553-1557. (International Geoscience and Remote Sensing Symposium (IGARSS)). Epub 2024 Sept 5. doi: 10.1109/IGARSS53475.2024.10640428

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

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