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Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
Univ Navarra, Sch Architecture, Pamplona, Spain.;Univ Navarra, Navarra Ctr Int Dev, Pamplona, Spain..
Univ Navarra, Fac Econ, Pamplona, Spain.;Univ Navarra, Data Sci & Artificial Intelligence Inst, Pamplona, Spain..
Univ libre Bruxelles ULB, Dept Geosci Environm & Soc, Brussels, Belgium..
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0002-0001-2058
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2022 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 95, article id 101820Article in journal (Refereed) Published
Abstract [en]

Many 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.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 95, article id 101820
Keywords [en]
Remote sensing, Urban footprint, Morphological analysis, GIS, Slums
National Category
Geotechnical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-313768DOI: 10.1016/j.compenvurbsys.2022.101820ISI: 000798149500004Scopus ID: 2-s2.0-85129807587OAI: oai:DiVA.org:kth-313768DiVA, id: diva2:1667576
Note

QC 20220610

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-25Bibliographically approved

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Georganos, Stefanos

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