Road　extraction　from　remote　sensing　images　is　very　important　in　navigation,　urban　planning,　traffic　manage-ment　and　other　fields.　Deep　learning　methods　have　achieved　great　success　in　computer　vision　tasks.　Therefore,　road　extraction　from　remote　sensing　images　using　deep　learning　methods　can　significantly　improve　the　road　extraction　accuracy.　However,　these　methods　generally　have　problems　such　as　low　road　extraction　accuracy,　slow　training　speed,　high　computational　complexity,　and　poor　road　topology　connectivity.　In　order　to　solve　the　above　issues,　we　propose　a　Swin-ResUNet+　structure　and　use　the　new　paradigm　Swin-Transformer　to　extract　roads　in　remote　sensing　images.　Specifically,　we　construct　an　Edge　Enhancement　module　based　on　residual　connection　and　add　this　module　to　each　stage　of　the　encoder,　which　can　obtain　the　edge　information　in　remote　sensing　images.　Based　on　the　Edge　Enhancement　module,　we　propose　a　Swin-ResUNet+　structure　in　order　to　better　capture　the　topology　of　roads.　On　the　Massachusetts　road　dataset,　our　model　has　the　least　computational　cost　with　only　less　than　one　percent　accuracy　decrease.　On　the　DeepGlobe2018　road　dataset,　our　model　not　only　has　the　least　computational　complexity　but　also　achieves　the　highest　values　of　mIOU,　mDC,　mPA　and　F1-score.　In　a　word,　Swin-ResUNet+　obtains　a　much　better　trade-off　between　accuracy　and　efficiency　than　previous　CNN-based　and　Transformer-based　methods.