| 1. |
- Abascal, Angela, et al.
(author)
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AI perceives like a local : predicting citizen deprivation perception using satellite imagery
- 2024
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In: npj Urban Sustainability. - : Springer Nature. - 2661-8001. ; 4:1
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Journal article (peer-reviewed)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.
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| 2. |
- Abascal, Angela, et al.
(author)
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Making Urban Slum Population Visible : Citizens and Satellites to Reinforce Slum Censuses
- 2024
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In: Urban Inequalities from Space. - : Springer. - 9783031491856 - 9783031491832 ; , s. 287-302
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Book chapter (peer-reviewed)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.
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| 3. |
- Georganos, Stefanos, et al.
(author)
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Introduction
- 2024
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In: Urban Inequalities from Space. - : Springer. - 9783031491856 - 9783031491832 ; , s. 1-9
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Book chapter (peer-reviewed)abstract
- This chapter discusses the challenges faced by low-and middle-income countries (LMICs) in dealing with rapid transformation processes, including increasing inequalities, overconsumption of natural resources, high urbanisation rates, massive environmental degradation, and the growing impacts of climate change. The Majority World, where most of the world’s population resides, is the epicentre of the ongoing urban transformation, but it lacks accurate, high-resolution, and timely data to support mitigation and adaptation processes. The article highlights the potential of Earth Observation (EO) data to address data gaps and tackle urban and environmental challenges in LMICs. The article discusses the advances in using AI and EO-based algorithms to measure and characterize urban and environmental inequalities, including climate change and environmental challenges, infrastructure inequalities, and mapping the morphology and dynamics of cities, sub-urban and peri-urban areas with EO. We emphasize the innovative use of existing datasets to provide locally relevant information to users and how EO can create societal impacts.
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| 4. |
- Kuffer, Monika, et al.
(author)
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Preface
- 2024
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In: Urban Inequalities from Space. - : Springer. - 9783031491856 - 9783031491832 ; , s. vii-viii
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Book chapter (peer-reviewed)
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| 5. |
- Vanhuysse, Sabine, et al.
(author)
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Putting the Invisible on the Map : Low-Cost Earth Observation for Mapping and Characterizing Deprived Urban Areas (Slums)
- 2024
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In: Urban Inequalities from Space. - : Springer. - 9783031491825 - 9783031491832 ; , s. 119-137
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Book chapter (peer-reviewed)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.
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