Lineaments mapping is an authentic issue for deciphering the tectonic setting, geological history, mineral prospecting, and other several applications. Consequently, the study objective is to examine various remote sensing datasets at wide various spatial resolutions (10, 12.5, 15, 20, 30 m), to recommend the best in lineaments elicitation, for usage in the geological scientific community. Toward this aim, nine various remote sensing datasets including optical sensors (Landsat OLI, ASTER, Earth Observing-1 Advanced Land Imager, Sentinel 2A), radar data (Sentinel 1), and digital elevation models (ALOS PALSAR, SRTM, NASA, ASTER V3), are tackled. In this scope, we created an entirely automatic lineament derivation environment through the integration of edge detection and line-linking algorithms. Results show that the used optical sensors are less efficient than DEMs having the same spatial resolution. Sentinel 1 radar data is more competent than optical data sources. ALOS PALSAR DEM (12.5m) is more eligible than any other utilized data type even sentinel 1 data (10 m). Wholly, DEMs built from radar data (e.g., PALSAR DEM) proved their leverage in lineament extraction to a limit that can deviate from the well-known relationship between the number of extracted lineaments and pixel size.