Abstract:  For high-resolution images，the road situation is complex.And there are narrow roads or roads separated by buildings and shadows，leading to the problem of low extraction accuracy.In this paper，an improved model AP-LinkNet combining the atrous convolutional element and the parallel attention mechanism module is proposed，which can achieve higher detail extraction accuracy by expanding the receptive field and paying deep attention to road features in the downsampling coding process.The atrous convolution module expands the receptive field without changing the relationship between pixels on space.The parallel attention mechanism increases the attention to channel and spatial information during input image sampling.Combining the characteristics of the two mechanisms，the noise disturbance of complex road background is reduced and the overall accuracy is improved.The experimental results in this paper are compared with DeepLabV3+，U-Net，LinkNet and D-LinkNet.The F1 score and IOU on the DeepGlobe dataset are 80.69% and 78.65%，respectively.And the F1 score is 11.71%，5.24%，3.97% and 3.58% higher than the comparison models.The results show that the proposed model has higher accuracy and robustness，and has a good effect on extracting the narrow and complex road details from high-resolution images.

Key words: deep learning, atrous convolution, parallel attention mechanism, mixture loss, convolutional neural networks