Monitoring the physical form of Informal Settlements in Cape Town during the Covid-19 Pandemic using Remote Sensing: The case of Spine Road, Khayelitsha, and Island Blue Downs, Cape Town, South Africa

Musa, Itumeleng

Monitoring the physical form of Informal Settlements in Cape Town during the Covid-19 Pandemic using Remote Sensing: The case of Spine Road, Khayelitsha, and Island Blue Downs, Cape Town, South Africa

Thesis / Dissertation

2026

Publisher

University of Cape Town

Department

School of Architecture, Planning and Geomatics

Faculty

Faculty of Engineering and the Built Environment

Abstract

Growing urbanisation has resulted in a significant demand for urban homes and different services that people need. As a result, there has been an upsurge in informal settlements in most South African cities. The number of new informal settlements increased during the COVID-19 outbreak of 2020-2023. The primary goal of this research is to use remote sensing to examine the physical form of new informal settlements in Cape Town that emerged over the three years of the COVID-19 outbreak. The study uses a case study approach by comparing the morphology of an informal settlement that was formed during the COVID-19 pandemic (Spine Road in Khayelitsha, popularly known as Social Distance) with one that was formed pre-COVID-19 (Island in Blue Downs). The study employs object-based classification to analyse the physical form of these new informal settlements in comparison to the previous informal settlements. The study also investigates pattern analysis using classification results to further analyse their pattern in comparison to informal settlements formed before the pandemic. The change detection approach was utilised to analyse their growth rate over the three years of the pandemic, and informal settlement densities were illustrated using heat maps. The study concluded that object-based classification was an effective strategy for monitoring the physical form and pattern of informal settlements. This is due to the method's great accuracy in distinguishing between shacks and other types of land cover. This allowed their pattern to be observed more easily. The hypothesis was that the new informal settlements were more dispersed and spread across a larger region than previous concentrated informal communities. The Average Nearest Neighbour (ANN) tool however demonstrated that the informal settlements formed during the pandemic were also clustered like the typical dense informal communities based on the nearest neighbour ratio of both settlements. The tool has challenges pertaining to the size of the land being analysed and are discussed extensively in the research. Conversely, object-based classification revealed that informal settlements that emerged during the COVID-19 pandemic grew at a slower rate than those formed before the outbreak. The dissertation advises that the municipality use these data and methodologies to monitor the growth of informal settlements to make decisions about service delivery and the upgrading of new informal settlements that may be located in suitable areas.