Mapping urban areas with remote sensing is particularly challenging due to the spatial spectral heterogeneity of human settlements and their complex dynamics related to land cover change. Sentinel-2 offers a unique opportunity to address this challenge by providing high-resolution multispectral imagery. This research aims to evaluate the potential of Sentinel-2 imagery for mapping human settlements, focusing on the accuracy and reliability of the results. The study will investigate the impact of different image processing techniques and machine learning algorithms on the mapping performance. The findings of this research will contribute to the development of more accurate and efficient methods for mapping human settlements using Sentinel-2 imagery.