Unsupervised deep clustering of high-resolution satellite imagery reveals phenotypes of urban development in Sub-Saharan Africa
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2025-08-01
Date Acceptance
2025-05-21
Citation
Science of the Total Environment, 2025, 988
ISSN
0048-9697
Publisher
Elsevier
Start Page
179739
End Page
179739
Journal / Book Title
Science of the Total Environment
Volume
988
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.scitotenv.2025.179739
Publication Status
Published
Article Number
179739
Date Publish Online
2025-06-05