Rapid urbanization is taking place in Low-and middle-income countries (LMICs). Often there is not sufficient data monitoring the quick urban change. This study explores the use of machine learning classification within remote sensing to foster sustainable urban practices in a secondary city in an LMIC. The aim is to extract spatially detailed land cover data and investigate its temporal evolution from 2018 to 2021. Furthermore, targeted interviews with residents were conducted to gain an in-situ understanding of the land cover changes. The research reveals a trend of increased impervious surface in Sekondi-Takoradi, especially around the urban outskirts. Some patterns of densification can also be identified, predominantly in urban areas with a mix of impervious surfaces and vegetation. These findings reveal similar land cover change patterns as previous remote sensing studies, a decrease in vegetation, and an increase in impervious surfaces.  The used method can be applied at a larger scale to monitor the urbanization of secondary cities in LMICs, a field that often is neglected. These insights can contribute to achieving the UN's 11th Sustainable Development Sustainable Cities and Communities.