Road　datasets　are　fundamental　and　imperative　for　traffic　management　and　urban　planning.　Different　high　resolution　optical　remote　sensing　images　are　widely　used　for　automatic　road　extraction　but　the　results　are　usually　limited　to　local　scale　and　spectral　confusions　in　barren　and　cropland,　while　accurate　large-scale　road　extraction　remains　challenging.　In　this　study,　we　incorporated　medium　resolution　optical　and　SAR　data,　i.e.,　10　meter　resolution　Sentinel-1　and　Sentinel-2,　for　road　extraction　at　a　large　scale　and　evaluated　the　contribution　of　different　data　sources.　We　developed　a　United　U-Net　(UU-Net)　to　fuse　optical　and　SAR　data　for　road　extraction,　which　was　trained　and　evaluated　on　a　large-scale　multisource　road　extraction　dataset.　The　UU-Net　achieved　better　accuracy　than　traditional　deep　convolutional　networks　with　optical　or　SAR　data　alone,　which　obtained　an　average　F1　of　0.5502　and　an　average　IoU　of　0.4021,　outperforming　in　160　out　of　200　(80%)　0.5-by-0.5　degree　evaluation　grids.　The　results　indicated　that　SAR　contributes　more　to　road　extraction　in　barren　land,　while　optical　data　contributes　more　to　large　slope　areas.　The　road　accuracy　is　positively　related　to　elevation　and　urban　percentage,　which　distributes　higher　in　eastern　China　and　lower　in　western.　The　road　centerline　from　10　m　road　showed　comparable　results　with　that　from　Open　Street　Map　(OSM),　indicating　its　promising　applications　to　support　large-scale　urban　transportation　studies.