More details may be lost and considerations of the surrounding environment of the road are inadequate when extract the road from GF-2 remote sensing satellite which based on the deep neural network. Aiming at these problems and based on the existing researches results, this study proposes an improvement proposal which using the full convolutional neural network to extract road from remote sensing images. The scheme innovatively researches the algorithm principle of the full convolutional neural network and outputs the pre-graded GF-2 images in a certain size. Then, the output images and the corresponding labels are input into the improved full convolutional neural network. At last, a road extraction image with higher recognition accuracy is obtained by combining residual unit and increasing the number of network layers. Experiments show that the effect on road extraction of GF-2 satellite images is improved in the same sample, the integrity and accuracy of the road are also improved.