Automatically　extracting　roads　from　remote　sensing　images　is　increasingly　important　for　autonomous　driving　and　geographic　information　systems.　However,　due　to　factors　such　as　ground　objects　blocking　road　boundaries　and　some　roads　being　narrow　and　long　in　high-resolution　remote　sensing　images,　it　is　difficult　for　the　current　mainstream　pixel-level　extraction　model　to　guarantee　the　continuity　of　roads　and　the　smoothness　of　boundaries.　To　solve　this　problem,　this　paper　designs　a　neural　network　Rse-net　based　on　semantic　segmentation,　using　a　two-stream　semantic　segmentation　network　algorithm　to　make　the　network　pay　more　attention　to　the　boundary　information　of　roads　and　narrow　roads.　This　structure　uses　shape　stream　to　process　road　boundary　information　(roads＇　shape　stream),　and　processes　it　in　parallel　with　classic　stream.　Use　the　Gated　Shape　CNN　to　connect　the　two　streams　of　roads＇　shape　stream　and　classic　stream,　and　use　the　classical　stream　to　optimize　the　boundary　information　in　the　shape　stream.　At　the　same　time,　a　multi-scale　convolutional　attention　mechanism　is　used　between　the　decoder　and　the　encoder　to　expand　the　receptive　field　through　large-core　attention,　and　obtain　more　semantic　information　without　causing　too　much　calculation.　Finally,　the　effectiveness　of　the　proposed　network　is　verified　by　comparative　experiments.