Evidence map›Paper›PMID 40480170›Full record

ArticleThe Science of the total environment2025

Unsupervised deep clustering of high-resolution satellite imagery reveals phenotypes of urban development in Sub-Saharan Africa.

A Barbara Metzler, Ricky Nathvani, Viktoriia Sharmanska, Wenjia Bai, Simon Moulds, Nkechi Srodah Owoo, Iris Ekua Mensimah Fynn, Emily Muller, Esaie Dufitimana, Ghafi Kondi Akara and 3 more

Abstract read
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In one paragraph

Article in The Science of the total environment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

A Barbara MetzlerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK.
Ricky NathvaniDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK.
Viktoriia SharmanskaDepartment of Informatics, University of Sussex, UK; Department of Computing, Imperial College London, London, UK.
Wenjia BaiDepartment of Computing, Imperial College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK.
Simon MouldsSchool of Geosciences, University of Edinburgh, UK.
Nkechi Srodah OwooDepartment of Economics, University of Ghana, Legon, Accra, Ghana.
Iris Ekua Mensimah FynnDepartment of Geography and Resources Development, University of Ghana, Legon, Accra, Ghana.
Emily MullerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK.
Esaie DufitimanaAfrican Institute for Mathematical Sciences Research and Innovation Centre, Kigali, Rwanda.
Ghafi Kondi AkaraAfrican Institute for Mathematical Sciences Research and Innovation Centre, Kigali, Rwanda.
George OwusuInstitute of Statistical, Social & Economic Research, University of Ghana, Accra, Ghana.
Samuel Agyei-MensahDepartment of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana.
Majid EzzatiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK; Regional Institute for Population Studies, University of Ghana, Accra, Ghana; Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK; Imperial Global Ghana Hub, Accra, Ghana. Electronic address: majid.ezzati@imperial.ac.uk.

Funding

Wellcome Trust 209376Wellcome Trust 227779
6 · The paper itself

Abstract

Sub-Saharan Africa and other developing regions have urbanized extensively, leading to complex urban features with varying presence and types of roads, buildings and vegetation. We use a novel hierarchical deep learning framework and high-resolution satellite images to characterize multidimensional urban environments in multiple cities. Application of the model to images from Accra, Dakar, and Dar es Salaam identified areas with analogous patterns of building density, roads and vegetation. These included dense settlements within the metropolitan boundary (20-54% of urban area), peri-urban intermix of natural and built environment (21-44%), natural vegetation (9-13%) and agricultural land (8-15%). Kigali, with its mountainous geography and post-colonial expansion, exhibited unique urban characteristics including a sparser urban core (23%) and significant wildland-urban intermix (19% of vegetation). Other notable clusters were water (2% of area of Accra) and empty land (8-10% of Accra and Dakar). Our results demonstrate that unlabeled satellite images with unsupervised deep learning can be used for consistent and coherent near-real-time urban monitoring, particularly in regions where traditional data are scarce.

Indexed as

Environmental MonitoringSatellite ImageryUrbanizationAfrica South of the SaharaCitiesDeep LearningPhenotypeDeepClusterHigh-resolution satellite imageryMachine learningRemote sensingSub-Saharan AfricaUnsupervised deep learningUrban phenotypes

Identifiers

PMID40480170
PMCPMC7617845

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.