Cloud computing services offering access to large volumes of Earth Observation (EO) data have been increasingly used combining programming languages and remote sensing techniques. Big data processing plays an important role in monitoring dynamic processes such us urban growth and climate change, since they take full advantage from the temporal resolution of EO satellites. This study tests the cloud-based computational platform of Digital Earth Africa (DEA) and its data cube on the historic city of Sfax (Tunisia). We used different urban indices to extract urban land in 2023 and then compared the results with the Global Human Settlement Layer (GHSL). The index with best accuracy was used, later, to track urban extent from 1985 to 2023 in correlation with Land Surface Temperature (LST) and Air Temperature (AT). The results show that the Enhanced Normalized Difference Impervious Surface Index (ENDISI) gave the best accuracy with GHSL and helped highlighting urban growth hotspots from 1985 to 2023.Temperature trends for the same period showed non homogenous variations with a global average increase. Correlation analysis revealed positive moderate correlation between both ENDISI and LST and ENDISI and AT. Therefore, it is proven that DEA is an ambitious program for African countries’ environmental studies offering free ARD and decision ready products in an intuitive platform. This study explored the HUL of Sfax city serving its world heritage registration project and helped developing and testing DEA functions.