Considering the problems of low segmentation efficiency of traditional image segmentation methods, complex and diverse features of remote sensing images, and limited segmentation performance in complex scenes, an improved U-Net model is proposed on the basis of the U-Net network architecture, which can satisfactorily extract the features of remote sensing images while maintaining efficiency. First, EfficientNetV2 is used as the encoding network of U-Net to enhance the feature extraction ability and improve the training and inference efficiency. Then, the convolutional structural re-parameterization method is applied in the decoding network and is combined with the channel attention mechanism to improve the network performance without increasing the inference time. Finally, the multi-scale convolution fusion module is employed to improve the feature extraction ability of the network for objects with different scales and the utilization of context information. The experiments reveal that the improved network can not only improve the segmentation performance of remote sensing images but also promote segmentation efficiency.