Aiming at the problems of small target building omission, target building misclassification and boundary bonding in remote sensing image building segmentation by DeepLabV3+, this paper proposes an improved remote sens- ing image building segmentation method for DeepLabV3 +. Firstly, an improved dense cavity pyramidal pooling DenseASPP module is used in the encoder stage to obtain larger sensory fields and denser feature pyramids, and a bar pooling module is added in parallel to enable the backbone network to make effective use of the long-range dependen- cies. Secondly, the SE channel attention module is introduced in the decoder stage to enhance the correlation between channels to obtain richer edge features. Finally, the optimised features from the SE module are fused with the original features to enhance the segmentation performance of the network. The experimental results on the WHU Building data- set show that the building segmentation results of this paper’s method achieve 92. 33% and 95. 54% in the intersection and merge ratio (Iou) and F1 index respectively.