Semi-supervised 'soft' extraction of urban types associated with deprivation

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dc.contributor.authorVanhuysse, Sabine
dc.contributor.authorAbascal, Ángela
dc.contributor.authorWang, Jon
dc.contributor.authorGeorganos, Stefanos
dc.contributor.authorKuffer, Monika
dc.contributor.authorWolff, Eléonore
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.date.accessioned2025-07-01T07:20:37Z
dc.date.available2025-07-01T07:20:37Z
dc.date.issued2024-09-05
dc.date.updated2025-07-01T06:25:38Z
dc.description.abstractMapping deprived urban areas in low- and middle-income countries is essential for policy development. While urban deprivation is a complex concept encompassing multiple dimensions, we propose an approach to capture its physical traits reflected in urban morphology, aiming for scalability. Our method makes use of affordable Earth Observation imagery and existing open geospatial datasets, and eliminates the need for manual labeling. It involves feature extraction, unsupervised learning, and pseudo-label based semi-supervised learning, resulting in 'soft' urban deprivation maps that avoid flagging areas as 'slums'. The study demonstrated its effectiveness in identifying the urban types associated with deprived areas at the scale of a large sub-Saharan African city.en
dc.description.sponsorshipThe research pertaining to these results received financial aid from the Belgian Federal Science Policy (BELSPO) according to the agreement of subsidy no. SR/11/405 (ONEKANA).
dc.format.mimetypeapplication/pdf
dc.identifier.citationVanhuysse, S., Abascal, A., Wang, J., Georganos, S., Kuffer, M., Wolff, E. (2024) Semi-supervised 'soft' extraction of urban types associated with deprivation. In 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1581-1584). IEEE. 979-8-3503-6033-2
dc.identifier.doi10.1109/IGARSS53475.2024.10642280
dc.identifier.isbn979-8-3503-6033-2
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/54352
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIn 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE; 2024. p. 1581-1584
dc.relation.publisherversionhttps://doi.org/10.1109/IGARSS53475.2024.10642280
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectSemi-supervised learningen
dc.subjectScalabilityen
dc.subjectMorphometricsen
dc.subjectSlumsen
dc.subjectUrban povertyen
dc.titleSemi-supervised 'soft' extraction of urban types associated with deprivationen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication57cd2d2d-0458-4193-8d60-f2d2227f17ae
relation.isAuthorOfPublication.latestForDiscovery57cd2d2d-0458-4193-8d60-f2d2227f17ae

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