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Objective: Semantic segmentation of high-resolution remote sensing image has important theoretical and practical value in the field of aerial image analysis. However, due to the richness of building semantics and the complexity of image background in high-resolution remote sensing images, the traditional segmentation methods are prone to edge blur, loss of detail information and low resolution. Method: To solve the problem of fuzzy boundary and information loss in high-resolution satellite image semantic segmentation, an end-to-end convolutional neural network named Dilated-UNet (D-UNet) has been proposed. Firstly, the U-Net network structure is improved and the multi-scale dilated convolution module of four channels is expanded by using the division technology. Each channel uses different convolution expansion rate to identify the multi-scale semantic information, so as to extract richer detailed information. Secondly, a joint loss function of cross entropy and Dice coefficient is designed to achieve the desired segmentation effect. Results: The model is comprehensively evaluated and tested on the Inria aerial image dataset. The experimental results show that the proposed remote sensing image segmentation method can effectively segment urban buildings at pixel level from high-resolution remote sensing images, and the segmentation accuracy is higher, which is better than other methods. Conclusions: We conclude that our proposed D-UNet has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios.