Abstract: The urban area estimation in dry and semi-dry climate, is still a difficult. Therefore, identify a reliable impervious index is critical. For that reason, Ain Azel city that located in semi-arid land of North-East Algeria, was the area of test of three impervious indices, namely: the Built-up Area Index (BAI), the Normalized Impervious Surface Index (NISI) and the Urban Area Index (UAI). These indices were derived from Sentinel-2 data and subjected to the Support Vector Machine (SVM) classification method, to extract built-up area class. The accuracy assessment results showed that the Overall accuracy (Oa) of the NISI index achieved 92,67 %, which is a little less compared to the (Oa) of UAI index which is about 93.67 %. Based on this, the both indices provided satisfactory result, although the UAI index, relatively, overestimated the built-up area. However, the BAI index that use the Blue and Near-Infrared bands is sufficiently discriminative between highly similar of buildings materials and dry soil; the BAI index produced the accurate built-up mapping with well detection of road networks with (Oa) achieved 95.67% and the highest kappa coefficient which is about 86.54 %. This is due to BAI band’s degree of sensitivity and their high spatial resolution of 10 m. Consequently, the SVM segmentation-based BAI index worked well and the result is promising for accurate modeling of cities with same environment condition, for its sustainable development. However, the performance level evaluation of indices applied in this study, should be retested over different regions in dry land.