Background:　Road　network　data　are　crucial　in　various　applications,　such　as　emergency　response,　urban　planning,　and　transportation　management.　The　recent　application　of　deep　neural　networks　has　significantly　boosted　the　efficiency　and　accuracy　of　road　network　extraction　based　on　remote　sensing　data.　However,　most　existing　methods　for　road　extraction　were　designed　at　local　or　regional　scales.　Automatic　extraction　of　large-scale　road　datasets　from　satellite　images　remains　challenging　due　to　the　complex　background　around　the　roads,　especially　the　complicated　land　cover　types.　To　tackle　this　issue,　this　paper　proposes　a　land　cover　background-adaptive　framework　for　large-scale　road　extraction.　Method:　A　large　number　of　sample　image　blocks　(6820)　are　selected　from　six　different　countries　of　a　wide　region　as　the　dataset.　OpenStreetMap　(OSM)　is　automatically　converted　to　the　ground　truth　of　networks,　and　Esri　2020　Land　Cover　Dataset　is　taken　as　the　background　land　cover　information.　A　fuzzy　C-means　clustering　algorithm　is　first　applied　to　cluster　the　sample　images　according　to　the　proportion　of　certain　land　use　types　that　obviously　negatively　affect　road　extraction　performance.　Then,　the　specific　model　is　trained　on　the　images　clustered　as　abundant　with　that　certain　land　use　type,　while　a　general　model　is　trained　based　on　the　rest　of　the　images.　Finally,　the　road　extraction　results　obtained　by　those　general　and　specific　modes　are　combined.　Results:　The　dataset　selection　and　algorithm　implementation　were　conducted　on　the　cloud-based　geoinformation　platform　Google　Earth　Engine　(GEE)　and　Google　Colaboratory.　Experimental　results　showed　that　the　proposed　framework　achieved　stronger　adaptivity　on　large-scale　road　extraction　in　both　visual　and　statistical　analysis.　The　C-means　clustering　algorithm　applied　in　this　study　outperformed　other　hard　clustering　algorithms.　Significance:　The　promising　potential　of　the　proposed　background-adaptive　network　was　demonstrated　in　the　automatic　extraction　of　large-scale　road　networks　from　satellite　images　as　well　as　other　object　detection　tasks.　This　search　demonstrated　a　new　paradigm　for　the　study　of　large-scale　remote　sensing　applications　based　on　deep　neural　networks.