| 1. |
- Abascal, Angela, et al.
(författare)
-
AI perceives like a local : predicting citizen deprivation perception using satellite imagery
- 2024
-
Ingår i: npj Urban Sustainability. - : Springer Nature. - 2661-8001. ; 4:1
-
Tidskriftsartikel (refereegranskat)abstract
- Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted the potential of Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects of urban deprivation. However, little research has explored AI’s ability to predict how locals perceive deprivation. This research aims to develop a method to predict citizens’ perception of deprivation using satellite imagery, citizen science, and AI. A deprivation perception score was computed from slum-citizens’ votes. Then, AI was used to model this score, and results indicate that it can effectively predict perception, with deep learning outperforming conventional machine learning. By leveraging AI and EO, policymakers can comprehend the underlying patterns of urban deprivation, enabling targeted interventions based on citizens’ needs. As over a quarter of the global urban population resides in slums, this tool can help prioritise citizens’ requirements, providing evidence for implementing urban upgrading policies aligned with SDG-11.
|
|
| 2. |
- Abascal, Angela, et al.
(författare)
-
Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
- 2022
-
Ingår i: Computers, Environment and Urban Systems. - : Elsevier BV. - 0198-9715 .- 1873-7587. ; 95
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 3. |
- Abascal, Angela, et al.
(författare)
-
Making Urban Slum Population Visible : Citizens and Satellites to Reinforce Slum Censuses
- 2024
-
Ingår i: Urban Inequalities from Space. - : Springer. - 9783031491856 - 9783031491832 ; , s. 287-302
-
Bokkapitel (refereegranskat)abstract
- In response to the “Leave No One Behind” principle (the central promise of the 2030 Agenda for Sustainable Development), reliable estimate of the total number of citizens living in slums is urgently needed but not available for some of the most vulnerable communities. Not having a reliable estimate of the number of poor urban dwellers limits evidence-based decision-making for proper resource allocation in the fight against urban inequalities. From a geographical perspective, urban population distribution maps in many low- and middle-income cities are most often derived from outdated or unreliable census data disaggregated by coarse administrative units. Moreover, slum populations are presented as aggregated within bigger administrative areas, leading to a large diffuse in the estimates. Existing global and open population databases provide homogeneously disaggregated information (i.e. in a spatial grid), but they mostly rely on census data to generate their estimates, so they do not provide additional information on the slum population. While a few studies have focused on bottom-up geospatial models for slum population mapping using survey data, geospatial covariates, and earth observation imagery, there is still a significant gap in methodological approaches for producing precise estimates within slums. To address this issue, we designed a pilot experiment to explore new avenues. We conducted this study in the slums of Nairobi, where we collected in situ data together with slum dwellers using a novel data collection protocol. Our results show that the combination of satellite imagery with in situ data collected by citizen science paves the way for generalisable, gridded estimates of slum populations. Furthermore, we find that the urban physiognomy of slums and population distribution patterns are related, which allows for highlighting the diversity of such patterns using earth observation within and between slums of the same city.
|
|
| 4. |
- Georganos, Stefanos, et al.
(författare)
-
Is It All the Same? : Mapping and Characterizing Deprived Urban Areas Using WorldView-3 Superspectral Imagery. A Case Study in Nairobi, Kenya
- 2021
-
Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:24
-
Tidskriftsartikel (refereegranskat)abstract
- In the past two decades, Earth observation (EO) data have been utilized for studying the spatial patterns of urban deprivation. Given the scope of many existing studies, it is still unclear how very-high-resolution EO data can help to improve our understanding of the multidimensionality of deprivation within settlements on a city-wide scale. In this work, we assumed that multiple facets of deprivation are reflected by varying morphological structures within deprived urban areas and can be captured by EO information. We set out by staying on the scale of an entire city, while zooming into each of the deprived areas to investigate deprivation through land cover (LC) variations. To test the generalizability of our workflow, we assembled multiple WorldView-3 datasets (multispectral and shortwave infrared) with varying numbers of bands and image features, allowing us to explore computational efficiency, complexity, and scalability while keeping the model architecture consistent. Our workflow was implemented in the city of Nairobi, Kenya, where more than sixty percent of the city population lives in deprived areas. Our results indicate that detailed LC information that characterizes deprivation can be mapped with an accuracy of over seventy percent by only using RGB-based image features. Including the near-infrared (NIR) band appears to bring significant improvements in the accuracy of all classes. Equally important, we were able to categorize deprived areas into varying profiles manifested through LC variability using a gridded mapping approach. The types of deprivation profiles varied significantly both within and between deprived areas. The results could be informative for practical interventions such as land-use planning policies for urban upgrading programs.
|
|
| 5. |
- Kuffer, Monika, et al.
(författare)
-
Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries : A User-Centered Approach
- 2021
-
Ingår i: URBAN SCIENCE. - : MDPI AG. - 2413-8851. ; 5:4, s. 72-
-
Tidskriftsartikel (refereegranskat)abstract
- Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urgently required for the production of EO-based mapping products. Combining an online survey and several forms of user interactions, we defined five system specifications (derived from user requirements) for designing an open-access spatial information system for deprived urban areas. First, gridded maps represent the optimal spatial granularity to deal with high uncertainties of boundaries of deprived areas and to protect privacy. Second, a high temporal granularity of 1-2 years is important to respond to the high spatial dynamics of urban areas. Third, detailed local-scale information should be part of a city-to-global information system. Fourth, both aspects, community assets and risks, need to be part of an information system, and such data need to be combined with local community-based information. Fifth, in particular, civil society and government users should have fair access to data that bridges the digital barriers. A data ecosystem on urban deprivation meeting these requirements will be able to support community-level action for improving living conditions in deprived areas, local science-based policymaking, and tracking progress towards global targets such as the SDGs.
|
|
| 6. |
- Vanhuysse, Sabine, et al.
(författare)
-
Putting the Invisible on the Map : Low-Cost Earth Observation for Mapping and Characterizing Deprived Urban Areas (Slums)
- 2024
-
Ingår i: Urban Inequalities from Space. - : Springer. - 9783031491825 - 9783031491832 ; , s. 119-137
-
Bokkapitel (refereegranskat)abstract
- It is estimated that more than half of city dwellers in sub-Saharan Africa currently live in deprived urban areas, often called slums or informal settlements, although these terms cover different urban realities. While the first target of Sustainable Development Goal (SDG) 11 is “to ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums,” there is a huge gap in timely spatial data to support evidence-based policies and monitor progress toward that objective. In this study, we document the potential of Earth Observation (EO) for mapping and characterizing deprived urban areas (DUAs) to narrow this gap. First, we provide a synthesis of user requirements that can be met without resorting to ancillary sources such as censuses and socioeconomic surveys, and we propose a list of cost criteria that should be minimized in EO workflows. Next, we present the city-scale and DUA-scale workflows that we developed based on three case studies and an assessment of their suitability for supporting pro-poor policies, in light of the cost criteria. We also share the main lessons learned and propose some avenues for future research.
|
|
