Urbanization in India has been rapid over the past few decades, which results in a substantial replacement of the natural surfaces into built-up lands. We present a performance evaluation of Sentinel-2B, Landsat-8 multispectral, and AVIRIS-NG hyperspectral imagery for extraction of road and roof surfaces using proposed spectral index-based and other conventional algorithms. The new road extraction index (NREI) and new building extraction index (NBEI) are developed for extraction of road and roof surfaces, respectively. Moreover, existing spectral angle mapper (SAM), spectral information divergence (SID), matched filter (MF), and support vector machine (SVM) are utilized as angle, information, filtering, and machine learning-based algorithms, respectively, for detection of both the surfaces. The results of our study suggest that the performance of AVIRIS-NG sensor is the best in comparison to aforesaid multispectral sensors, whereas Sentinel-2B performs better in comparison to Landsat-8 for extraction of road and roof surfaces. The comparison of various algorithms suggests that proposed indices, MF, and SVM produce the best results for extraction of road and roof surfaces, while SAM and SID are superior algorithms for extraction of both the surfaces in AVIRIS-NG imagery. Further, NREI and MF performed well for extraction of roads followed by NBEI and SAM for roofs in Landsat-8. Finally, NREI, SAM, and SID are found to be efficient for extraction of roads subsequently NBEI, SAM, and SID for roofs in Sentinel-2B imagery.