Buildings are integral components of urban areas. Extracting buildings from high-resolution remote sensing data holds significant academic importance in areas such as land use analysis， urban planning， and disaster risk reduction. For the problems of building extraction， an improved Mask R-CNN building instance segmentation model is proposed. Based on the residual neural network fusion convolutional attention model， a residual convolutional attention network is constructed to improve the problem of inadequate feature extraction. The loss function is optimized by adding the Dice Loss method， and then the feature learning process is optimized. And a post-processing strategy combining Douglas-Peucker algorithm and Fine polygon regularization algorithm is introduced to make the building contours more regular and smooth. The experimental results show that the improved model improves the detection mAP value by 7.74% at Iou 0.5 and 7.57% at Iou 0.75 compared with the original model， and the post-processing strategy improves the F1-Score value by 6.01% compared with the original model after selecting the appropriate threshold to optimize the mask. The instance segmentation model coupled with Mask R-CNN and attention mechanism improves the small building misdetection and omission problem， building segmentation boundary adhesion problem， and building segmentation accuracy； building post-processing strategy， improves building regularization.