Aiming　at　the　problems　of　low　building　segmentation　accuracy　and　blurred　edges　in　high-resolution　remote　sensing　images,　an　improved　fully　convolutional　neural　network　is　proposed　based　on　the　SegNet　network.　First,　GELU,　which　performs　well　in　deep　learning　tasks,　is　selected　as　the　activation　function　to　avoid　neuron　deactivation.　Second,　the　improved　residual　bottleneck　structure　is　used　in　the　encoding　network　to　extract　more　building　features.　Then,　skip　connections　are　used　to　fuse　images　The　low-level　and　high-level　semantic　features　are　used　to　assist　image　reconstruction.　Finally,　an　improved　edge　correction　module　is　connected　at　the　end　of　the　decoding　network　to　further　correct　the　edge　details　of　the　building　and　improve　the　edge　integrity　of　the　building.　Experiments　are　carried　out　on　the　Massachusetts　building　dataset,　and　the　precision　rate,　recall　rate,　and　F1　value　reach　93.5%,　79.3%,　and　81.9%,　respectively,　and　the　comprehensive　evaluation　index　F1　value　is　improved　by　about　5%　compared　with　the　basic　network.