Current　mainstream　deep　learning　network　models　have　many　problems　such　as　inner　cavity,　discontinuity,　missed　periphery,　and　irregular　boundaries　when　applied　to　building　extraction　from　high　spatial　resolution　remote　sensing　images.　This　paper　proposed　the　RMAU-Net　model　by　designing　a　new　activation　function　(Activate　Customized　or　Not,　ACON)　and　integrating　residuals　block　with　channel-space　and　criss-cross　attention　module　based　on　the　U-Net　model　structure.　The　ACON　activation　function　in　the　model　allows　each　neuron　to　be　activated　or　not　activated　adaptively,　which　helps　improve　the　generalization　ability　and　transmission　performance　of　the　model.　The　residual　module　is　used　to　broaden　the　depth　of　the　network,　reduce　the　difficulty　in　training　and　learning,　and　obtain　deep　semantic　feature　information.　The　channel-spatial　attention　module　is　used　to　enhance　the　correlation　between　encoding　and　decoding　information,　suppress　the　influence　of　irrelevant　background　region,　and　improve　the　sensitivity　of　the　model.　The　cross　attention　module　aggregates　the　context　information　of　all　pixels　on　the　cross　path　and　captures　the　global　context　information　by　circular　operation　to　improve　the　global　correlation　between　pixels.　The　building　extraction　experiment　using　the　Massachusetts　dataset　as　samples　shows　that　among　all　the　7　comparison　models,　the　proposed　RMA-UNET　model　is　optimal　in　terms　of　intersection　of　union　and　F1-score,　as　well　as　indexes　of　precision　and　recall,　and　the　overall　performance　of　RMAU-Net　is　better　than　similar　models.　Each　module　is　added　step　by　step　to　further　verify　the　validity　of　each　module　and　the　reliability　of　the　proposed　method.　©2022,　Science　Press.　All　right　reserved.