Nighttime　illuminated　roads　(NTIRs)　are　closely　related　to　residents＇　nighttime　travel　and　activities.　However,　traditional　nighttime　light　(NTL)　data　are　not　highly　applicable　for　large-scale　NTIR　extraction　tasks　because　of　challenges　such　as　low　resolution　and　data　acquisition.　In　contrast,　the　open-sourced　SDGSAT-1　NTL　data　released　in　2021　which　contains　a　panchromatic　band　(PAN)　of　10-m　resolution　and　red,　green,　blue　bands　(RGB)　of　40-m　resolution,　are　more　suitable　for　this　task.　To　date,　research　on　NTIR　extraction　methods　is　still　insufficient,　and　the　application　potential　of　NTIR　products　has　not　been　fully　explored.　In　this　work,　a　deep　learning　model　called　DSC-UNet　was　proposed　for　NTIR　extraction　via　SDGSAT-1　NTL　imagery　of　10-m　spatial　resolution.　The　proposed　model　uses　UNet　as　the　basic　architecture　and　incorporates　a　dynamic　snake　convolution　module　to　increase　the　sensitivity　of　the　NTIR　pixels.　Our　experimental　results　showed　that　DSC-UNet　outperformed　seven　baseline　models.　Using　the　proposed　model　and　vectorization　tools,　a　high-accuracy　NTIR　centerline　product　of　the　BTH　region　was　generated.　By　applying　spatial　statistics,　the　road　illumination　rate　of　the　BTH　region　was　assessed.　The　assessment　results　revealed　an　imbalance　in　road　lighting　levels　among　different　cities　in　the　BTH　region.