Previous　road　extraction　research　based　on　remote　sensing　data　mainly　used　traditional　optical　remote　sensing　imagery　acquired　during　the　daytime,　but　some　roads　on　the　images　were　affected　by　problems　such　as　building　shadows　and　similar　spectral　features　of　road　with　other　materials,　which　reduced　the　accuracy　of　road　extraction.　The　Glimmer　Imager　(GI)　equipped　on　the　Sustainable　Development　Science　Satellite　1　(SDGSAT-1)　provides　nighttime　light　(NTL)　images　with　relatively　high　spatial　resolution　(RGB:　40m;　PAN:　10m),　which　show　detail　of　road　networks.　In　order　to　explore　the　potential　of　the　NTL　data　obtained　from　SDGSAT-1　on　road　extraction,a　deep　learning　framework　combining　ResUnet　and　Dynamic　Snake　Convolution　(DSC)　module　was　proposed　in　this　study.　The　experimental　results　demonstrated　the　effectiveness　of　SDGSAT-1　NTL　on　road　extraction　tasks　and　the　extracted　roads　can　be　used　for　road　dataset　updating.