The　reliable　road　network　plays　a　vital　role　in　many　applications.　Owing　to　the　development　of　remote　sensing　technology　and　the　success　of　deep　learning　in　computer　vision,　automatic　road　extraction　from　remote　sensing　images　is　a　research　hotspot　in　recent　years.　However,　due　to　the　complicated　image　background　and　special　road　structure,　the　results　of　automatic　road　extraction　are　still　far　from　perfect.　In　this　paper,　we　propose　a　road　segmentation　network　that　is　designed　based　on　improved　U-Net,　which　contains　an　encoder　and　a　decoder.　First,　the　recurrent　criss-cross　attention　module　(CCA)　is　introduced　into　the　encoder　to　obtain　long-range　contextual　dependencies　with　a　relatively　small　number　of　computations　and　parameters,　which　results　in　better　understanding　and　expression　of　image　information.　Second,　we　propose　the　attention-based　multi-scale　feature　fusion　module　(AMS)　to　resolve　the　problem　of　different　shapes　and　widths　of　the　roads,　which　is　placed　between　the　encoder　and　decoder　and　uses　attention　mechanisms　to　guide　multi-scale　information　fusion.　Experimental　on　the　Massachusetts　Roads　Dataset　show　that　the　proposed　method　achieves　better　performance　in　road　extraction　than　other　methods　in　terms　of　precision,　recall,　F1-score,　and　accuracy.　©　2021,　Springer　Nature　Switzerland　AG.