Nighttime road lighting is crucial for transportation and substantially contributes to power consumption. To enhance energy efficiency, numerous Light Emitting Diode (LED) lamps have been deployed across urban road networks. Assessing their effectiveness, however, has been challenging due to the coarse spatial resolution of traditional glimmer imagery. This study leverages high-resolution imagery from the newly launched SDGSAT-1 satellite, equipped with a Glimmer sensor, to quantitatively evaluate the impact of LED integration on power conservation within urban road networks. The SDGSAT-1 satellite provides unprecedented clarity with 10 m panchromatic and 40 m multi-spectral RGB resolutions, enabling a detailed analysis of illuminated road networks and the differentiation of LED and non-LED lighting sources. We utilized an unsupervised machine learning approach to extract and categorize lighting networks from panchromatic images based on spectral characteristics in RGB images, achieving an F1-measure of up to 98.11% after field validation. Our results reveal substantial urban nightlife vibrancy and effective energy-saving strategies in the Guangdong-Hong Kong-Macao Greater Bay Area and the Yangtze River Delta, in contrast to older, economically developed cities where conservation efforts were less effective. This study underscores the potential of SDGSAT-1 imagery for precise nighttime lighting assessments and offers valuable insights for optimizing urban development and energy conservation policies.