Vanhuysse, SabineAbascal, ÁngelaWang, JonGeorganos, StefanosKuffer, MonikaWolff, Eléonore2025-07-012025-07-012024-09-05Vanhuysse, 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. 979-8-3503-6033-2979-8-3503-6033-210.1109/IGARSS53475.2024.10642280https://academica-e.unavarra.es/handle/2454/54352Mapping 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.application/pdfeng© 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.Semi-supervised learningScalabilityMorphometricsSlumsUrban povertyinfo:eu-repo/semantics/conferenceObject2025-07-01info:eu-repo/semantics/openAccess