Full metadata record
DC FieldValueLanguage
dc.creatorAbascal, Á. (Ángela)-
dc.creatorRodríguez-Carreño, I. (Ignacio)-
dc.creatorVanhuysse, S. (Sabine)-
dc.creatorGeorganos, S. (Stefanos)-
dc.creatorSliuzas, R. (Richard)-
dc.creatorWolff, E. (Eléonore)-
dc.creatorKuffer, M. (Monika)-
dc.date.accessioned2022-08-04T09:54:03Z-
dc.date.available2022-08-04T09:54:03Z-
dc.date.issued2022-
dc.identifier.citationAbascal, Á. (Ángela); Rodríguez-Carreño, I. (Ignacio); Vanhuysse, S. (Sabine); et al. "Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas". Computers, Environment and Urban Systems. (95), 2022, 101820es
dc.identifier.issn0198-9715-
dc.identifier.urihttps://hdl.handle.net/10171/63865-
dc.description.abstractMany cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as ‘slums’) which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.es_ES
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/11/380. (SLUMAP), from the NWO grant number VI. Veni. 194.025 and from the GCRF Digital Innovation for Development in Africa panel (EPSRC Reference: EP/T029900/1). Stefanos Georganos is supported by a Digital Futures postdoctoral fellowship grant.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRemote sensinges_ES
dc.subjectUrban footprintes_ES
dc.subjectMorphological analysises_ES
dc.subjectGISes_ES
dc.subjectSlumses_ES
dc.titleIdentifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areases_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.noteThis is an open access article under the CC BY licensees_ES
dc.identifier.doi10.1016/j.compenvurbsys.2022.101820-
dadun.citation.number95es_ES
dadun.citation.publicationNameComputers, Environment and Urban Systemses_ES
dadun.citation.startingPage101820es_ES

Files in This Item:
Thumbnail
File
1-s2.0-S0198971522000643-main.pdf
Description
Size
9.69 MB
Format
Adobe PDF


Statistics and impact

Items in Dadun are protected by copyright, with all rights reserved, unless otherwise indicated.