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Combining object-based machine learning with long-term time-series analysis for informal settlement identification

Fallatah, A, Jones, S, Wallace, L ORCID: 0000-0002-4642-8374 and Mitchell, M 2022 , 'Combining object-based machine learning with long-term time-series analysis for informal settlement identification' , Remote Sensing, vol. 14, no. 5 , p. 1226 , doi:

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Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and diverse group of indicators in a comprehensive ML framework.

Item Type: Article
Authors/Creators:Fallatah, A and Jones, S and Wallace, L and Mitchell, M
Keywords: informal settlement indicators, urbanisation, sustainable development goals (SDGs), machine learning (ML), very-high-resolution (VHR), time-series analysis, object-based image analysis (OBIA)
Journal or Publication Title: Remote Sensing
Publisher: MDPI AG
ISSN: 2072-4292
DOI / ID Number:
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Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 4.0 International (CC BY 4.0) license (

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