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Towards how to solve the problem that traditional deep neural networks model is difficult to accurately extract the edge contour features of buildings and cannot adaptively extract buildings of different sizes, a method of automatic segmentation of remote sensing image buildings on account of Multiscale-feature fusion Deep Neural Networks with dilated convolution (MDNNet) is proposed. To begin with, expansion convolution is introduced into ResNet101 model to expand the extraction field and preserve more feature image resolution. Secondly, multiscale feature fusion module is used to obtain building features of multiple scales and fuse features of different scales. Eventually, the feature decoding module is used to restore the feature image to the original input image size, thus realizing accurate segmentation of remote sensing image buildings. The experimental results on WHU Building change detection dataset show that the proposed model effectively overcomes the influence of road, trees and shadows, and the segmentation results effectively retain the detailed information of building boundaries and improve the segmentation accuracy. The pixel accuracy PA comes to 0.864, the mean Intersection over Union mIoU comes to 0.815 and the Recall rate comes to 0.862.