Towards an Indicator-Based Morphological Informality Model for Sub-Saharan Africa Using Open Building Footprint and Road Data (Version 1)

This study addresses the challenge of accurately mapping informal settlements, which are home to over a billion people globally. Current maps often simplify these areas into binary categories, ignoring the nuanced dimensions of deprivation. The research focuses on ”unplanned urbanization,&...

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Main Authors: S. Hafner, Q. Zhao, A. Abascal, M. Comerio de Paulo, G. Tregonning, A. Middleton, A. Shonowo, M. Kuffer, R. Engstrom, D. R. Thomson, F. C. Onyambu, C. Kabaria, P. Elias, O. Odulana, B. Alugbin, K. Baruwa, J. Porto de Albuquerque
Format: Article
Language:English
Published: Copernicus Publications 2025-05-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://isprs-archives.copernicus.org/articles/XLVIII-M-7-2025/221/2025/isprs-archives-XLVIII-M-7-2025-221-2025.pdf
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Summary:This study addresses the challenge of accurately mapping informal settlements, which are home to over a billion people globally. Current maps often simplify these areas into binary categories, ignoring the nuanced dimensions of deprivation. The research focuses on ”unplanned urbanization,” a key domain in informal settlement mapping, and proposes a method to classify morphological informality into three deprivation levels (low, medium, and high) based on two subdomains: small, dense structures (SDS) and irregular settlement layouts (ISL). The methodology involves analyzing building footprints and road network data using urban morphometrics, clustering these metrics into subdomains with k-means, and validating results with community-sourced reference data. Tested in Nairobi, Kenya, and Lagos, Nigeria, the model achieves good performance (F1 > 65 for indicator maps) but faces challenges in the medium informality class, particularly in Nairobi, where community feedback diverges significantly. Despite an overall accuracy of 48 % for Nairobi and 60 % for Lagos, the model offers a framework for continuous improvement. This work highlights the value of integrating local perspectives into mapping efforts and provides a scalable, transferable approach for identifying levels of morphological informality.
ISSN:1682-1750
2194-9034