Extreme heat events pose risks to human health, this risk can be further exacerbated by socio-economic dynamics and differences in environmental exposure in urban settings. Understanding how heat-stress vulnerability varies across urban populations is important for planning mitigation and adaptation responses by authorities. Different demographic groups, delineated by age, gender, living conditions, occupation, income, nationality, health and wellbeing tend to have different health outcomes during extreme heat events. Evidence suggests that these social determinants play a vital role in understanding the health outcomes of different sub-populations. While heat-stress vulnerability is well studied and understood in developed countries, there is still a gap to be filled by developing countries on how their unique contexts influence what is currently understood on how socio-economic and environmental dynamics influence heat-stress and the subsequent health impacts. Developing appropriate health sector responses and adaptive interventions relies on identifying these vulnerable populations along with their level of environmental risk. Socio-economic factors such as poverty, food and water insecurity, and limited access to healthcare facilities perpetuate vulnerability among these communities. To address the lack of understanding of the environmental risk impacts on the changing epidemiology in sub-Saharan Africa, the proposed work outlines a framework to quantify the intra-urban socio-economic and environmental vulnerability across African cities through a fixed threshold-based, spatial multicriteria analysis approach. Our results describe the process of developing a vulnerability metric from the combined environmental and socio-economic datasets through a geospatial analysis platform. We structure our analysis to assess the hypothesis that people living in areas which are densely populated, highly built-up and which have low vegetation cover are more susceptible to the impacts of extreme heat events and adverse health outcomes. Considering both environmental and socio-economic factors such as population density and access to healthcare services, allows us to better understand the subtleties and inequities of vulnerability. Geospatial approaches including remote sensing data have been recognized for their utility in providing instantaneous and synoptic views of extreme weather events. There are various challenges in acquiring high resolution environmental datasets, primarily, limitations in meteorological station data due to sparse spatial and temporal coverage. Hence, we make use of high-resolution and multi-temporal, satellite images to derive variables such as; Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Urban Thermal Field Variance Index (UTFVI). We implement this framework for Gauteng, a highly urban province in South Africa, which covers a land area of 18 176 km2 , and has a population of approximately 16.1 million. Preliminary results for Johannesburg, are presented as detection of vulnerability hotspots, highlighting areas of most concern for extreme heat events, based on their combined environmental and socio-economic context. The framework will be extended to similar cities across sub-Saharan Africa.