Machine Learning-Based Mapping of Slums and Analysis Of Slums Patterns: A Case Study Of Kampala District, Uganda. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda.

Abstract

Urban planning and policy-making face considerable challenges as a result of the increasing urbanization and rise of informal settlements in many emerging nations. Effective urban management requires accurate and current knowledge of slum regions' topographies. Therefore, the goal of this research project was to create a machine learning-based method for mapping slums and examining their landscape patterns. Sentinel 2 images, a random forest classifier, and primary geographic data like OpenStreetMap data were all employed to achieve this. The data-driven approach's overall categorization accuracy was 86%. The Slum pattern assessment also depicted a linear spatial pattern from the distribution of the slum patches all over the study area. The results further showed that the combination of machine learning for texture analysis, and landscape metrics such as patch density, fragmentation, and connectivity allow for a deeper understanding of the landscape patterns of slums and their implications for urban planning and development. All the developed methodologies and findings from this research are applicable to similar urban contexts, aiding decision-making processes and promoting evidence-based interventions for slum areas worldwide.