To　contribute　to　the　current　research　of　building　extraction　based　on　deep　learning　and　high-resolution　remote　sensing　images,　we　propose　an　improved　Unet　network　(Res_AttentionUnet),　which　combines　the　Residual　module　of　ResNet　and　Attention　mechanism.　We　apply　the　Unet　network　to　the　extraction　of　buildings　from　high-resolution　remote　sensing　images,　which　effectively　improves　the　extraction　accuracy　of　buildings.　The　specific　optimization　method　can　be　divided　into　three　parts.　Firstly,　in　the　traditional　Unet　semantic　segmentation　network　convolution　layer,　the　ResBlock　module　is　added　to　enhance　the　extraction　of　low-level　and　high-level　features.　Meanwhile,　the　Attention　mechanism　module　is　added　to　the　network　step　connection　part.　Secondly,　in　the　whole　net,　the　ResBlock　module　enables　the　convoluted　feature　map　to　obtain　more　bottom　information　and　enhance　the　robustness　of　the　convolution　structure,　so　as　to　prevent　underfitting.　Thirdly,　the　Attention　mechanism　can　enhance　the　feature　learning　of　building　area　pixels,　making　feature　extraction　more　complete,　so　as　to　improve　the　accuracy　of　building　extraction.　In　this　study,　we　use　the　open　data　set　(WHU　Building　Dataset),　provided　by　Ji　Shunping　team　of　Wuhan　University,　as　the　experimental　data　and　select　three　experimental　areas　with　different　building　characteristics　and　representativeness.　Then,　we　preprocess　the　different　experimental　areas　(including　sliding,　cropping,　and　image　enhancement,　etc.).　Finally,　we　use　four　different　network　models　of　Unet,　ResUnet,　AttentionUnet,　and　Res_AttentionUnet　to　extract　buildings　from　three　different　experimental　areas.　The　experimental　results　are　cross-compared　and　analyzed.　The　experimental　results　show　that,　compared　with　the　other　three　networks,　the　Res_AttentionUnet　proposed　in　this　paper　has　higher　accuracy　in　the　building　extraction　from　high-resolution　remote　sensing　images.　The　average　extraction　accuracy　of　Res_AttentionUnet　is　95.81%,　which　is　17.94%　higher　than　the　original　Unet　network,　and　2.19%　higher　than　ResUnet　(the　Unet　with　only　residual　module).　The　results　demonstrate　that　Res_AttentionUnet　can　significantly　improve　the　effectiveness　of　building　extraction　in　high-resolution　remote　sensing　images.　©　2021,　Science　Press.　All　right　reserved.