BackgroundSpatial accessibility to healthcare is a critical factor in ensuring equitable health outcomes. While studies on a global, continental, and national level exist, our understanding of intra-urban differences, particularly between formal and informal areas within cities in sub-Saharan Africa, remains limited.MethodsThis study integrates openly available datasets on land use in 19 sub-Saharan cities, healthcare facilities in the region, and street networks from OpenStreetMap. Using these datasets, we calculate service areas around hospitals, considering travel times ranging from 1 to 120 minutes with walking as the mode of travel. The resulting service areas are then merged with population data from WorldPop, allowing us to assess the proportion of the population with specific travel times to healthcare facilities from informal and formal residential areas.ResultsOur analysis reveals that 33% of the urban population can reach hospitals within 15 minutes, 58% within 30 minutes, and 78% within 60 minutes. Importantly, for some cities, we observe significant differences between formal and informal areas, with informal areas experiencing a disadvantage in terms of spatial accessibility to healthcare facilities. The population in informal areas is particularly disadvantaged in medium-sized cities.ConclusionsThis study sheds light on the spatial accessibility of healthcare facilities in sub-Saharan African cities, emphasizing the need to consider intra-urban disparities, particularly in informal areas. The findings underscore the importance of targeted interventions and urban planning strategies to address these disparities and ensure that healthcare services are accessible to all segments of the urban population.

Current environmental crises call for an integrated knowledge of landscapes and their ecosystems in a broader sense. This article presents a pedagogical framework for cross-disciplinary landscape research at postgraduate level. The framework is grounded in the use of geospatial epistemic discomfort as a creative force to develop and enhance inquiry skills able to cross and merge disciplinary boundaries. Developed within the Erasmus+ KA2 project “CROSSLAND”, the pedagogical framework is based on the scaffolding of epistemic discomfort through four key didactic elements: 1) cross-disciplinary group work and open-ended assignment, 2) in-field inquiry as pre-training on space-time, 3) replacement of traditional lectures by student-led seminars, 4) GIS labs centred on the exploration of cross-disciplinary portfolios of geospatial approaches and methods given as worked-out examples. Main results from the evaluation of the framework implementation in a Summer School show how learning cross-disciplinarity happened thanks to a scaffolding that allowed, first and foremost, the socialisation of different conceptualisations of space. While students felt at ease with geospatial epistemic discomfort, we can conclude that spatial cognitive processes are powerful in improving abilities beyond the spatial domain. 

Monitoring change in informal settlements remains a critical challenge, particularly in data-scarce contexts across the Global South. While satellite remote sensing provides strong temporal coverage, conventional approaches for mapping the built environment often rely on very high-resolution imagery or LiDAR, which lack consistent temporal availability and are costly to scale especially for capturing vertical growth. This study leverages Google’s Open Buildings 2.5D Temporal Dataset (2016-2023), which offers annual estimates of building presence, count, and height, to detect structural change in Nairobi, Kenya. By analysing differences in building count and average height across 100-meter grid cells, we developed a rule-based framework to identify four key transformation types: vertical densification, horizontal densification, combined densification (increase in both count and height), and decline. To our knowledge, this is the first study to use this dataset to assess vertical change within informal settlements. Validation was conducted through a two-source approach using historical satellite imagery (Google Earth Pro) and archival street-level imagery (Google Street View). A total of 154 grid cells across 13 slum areas were manually assessed, yielding an overall accuracy of 96.75%. Horizontal and combined densification showed perfect agreement, while vertical densification and decline categories had over 80% accuracy. Spatial analysis across slums, adjacent buffer areas, and the broader city revealed horizontal densification as the dominant trend within informal settlements, while vertical and combined growth were more prominent in surrounding zones. These results demonstrate the potential of Google’s 2.5D dataset for scalable, interpretable urban monitoring in rapidly changing environments.

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.

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years,there has been a signifcant increase in the development of spatially gridded population datasets.Despite these datasets often using similar input data to derive population fgures, notable diferencesarise when comparing them with direct ground-level observations. This study evaluates the precisionand accuracy of food exposure assessments using both known and generated gridded populationdatasets in Sweden. Specifcally focusing on WorldPop and GHSPop, we compare these datasetsagainst ofcial national statistics at a 100 m grid cell resolution to assess their reliability in foodexposure analyses. Our objectives include quantifying the reliability of these datasets and examiningthe impact of data aggregation on estimated food exposure across diferent administrative levels.The analysis reveals signifcant discrepancies in food exposure estimates, underscoring the challengesassociated with relying on generated gridded population data for precise food risk assessments.Our fndings emphasize the importance of careful dataset selection and highlight the potential foroverestimation in food risk analysis. This emphasises the critical need for validations against groundpopulation data to ensure accurate food risk management strategies.

Many studies are pointing to the fact that cities are experiencing higher temperatures than non-built-up areas. Yet limited can be found on thermal inequalities in the context of vulnerable groups, specifically linked to people living in deprivation. Here, we study heat patterns across vulnerable groups living in deprivation as an important effort that should be paralleled to the other urban climate studies and aim at answering two primary questions: (1) how temperature varies within and across deprived areas, and (2) what the key driving factors are for such variation. We conduct intensive in-situ measurements by involving local residents in air temperature traverse across deprived neighbourhoods and modelling the pattern of air temperature with spatial covariates. We also compare different modelling techniques while securing the interpretability of the air temperature pattern by using understandable spatial covariates, which is especially informative for mitigation and adaptation, and linking scientific exploration and practical solutions. 

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. 

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. 

Africa, as a major climate change hotspot, faces severe impacts, including extreme temperatures. Notably, urban areas are unequally affected by these impacts. The urban poor are particularly vulnerable to extreme temperatures, because of the environmental and physical characteristics of their neighbourhoods, and their limited resources to develop coping strategies. Limited knowledge exists of the spatial patterns of thermal inequalities within neighbourhoods. Our overall scientific objective is to explore the potential of Earth Observation (EO) to study how and why urban dwellers in the Global South (focusing on Africa) with different levels of deprivation are divergently exposed to varying temperatures and extreme heat, and to quantify the urban population exposed to such conditions. We make use of several state-of-the-art EO/AI models, and employ innovative in situ data collection methods together with local stakeholders through Citizen Science. We rely as far as possible on open or low-cost satellite imagery (e.g., Sentinel-1/2, Landsat, ECOSTRESS) for scalability and transferability, and we implement Machine Learning (ML) methods, including Deep Learning (DL). Results highlight significant local differences in thermal exposure, emphasizing the need to understand and communicate these spatial patterns to support the development of cost-effective adaptation strategies. 

Floods are one of the most destructive disasters worldwide and although they largely happen in rural, ruther than in urban areas, it is in the urban areas that substantial destruction of infrastructures is observed. Thus, cost effective methods to monitor flood damage and extent are required. In this paper, we investigate the implementation of U-Net on satellite and drone image dataset such as xBD and EDDA for building damage assessment in Mozambique. The recently published dataset EDDA was created by the National Institute for Disaster Management (INGD) and comprises drone imagery of Beira, in Mozambique. Using them, we obtained a dice score of 0.76 on building localization (BL) and mean intersection over the union (mIoU) of 0.54 on damage classification (DC). These are promising results considering that many datasets lack detailed information on African buildings. We also use some pre-trained models models such as ResNet for BL and DC.