TY - JOUR
T1 - Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
AU - Abascal, Angela
AU - Rodríguez-Carreño, Ignacio
AU - Vanhuysse, Sabine
AU - Georganos, Stefanos
AU - Sliuzas, R.
AU - Wolff, Eleonore
AU - Kuffer, M.
N1 - Funding Information:
The 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.
Funding Information:
The 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.
Publisher Copyright:
© 2022 The Authors
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - GIS
KW - Morphological analysis
KW - Remote sensing
KW - Slums
KW - Urban footprint
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
U2 - 10.1016/j.compenvurbsys.2022.101820
DO - 10.1016/j.compenvurbsys.2022.101820
M3 - Article
AN - SCOPUS:85129807587
SN - 0198-9715
VL - 95
SP - 1
EP - 20
JO - Computers, environment and urban systems
JF - Computers, environment and urban systems
M1 - 101820
ER -