High-resolution remote sensing (HRS) images are widely used in regional planning and mapping, and the timely update of building change information is essential. Although HRS images offer rich surface information for building change detection, the complexity of the building background and its varying nature require the network to capture both local and global information. To address this issue, the efficient-UNet++ change detection network is designed with UNet++ as the baseline model and EfficientNet-B1 as the backbone network to retain more semantic information. To extract features efficiently and reduce the network parameters, some standard convolutions are replaced with depthwise separable convolution (DW Conv). The network is also equipped with multi-headed self-attention to improve its ability to capture global information. The proposed methods achieve an intersection over union of 0.7921 and an F1 score of 0.8804 on the LEVIR-CD dataset.