Road extraction from remote sensing images is of great significance in promoting urban and rural development planning and construction.However,the traditional methods for road extraction from remote sensing images have the problems of large engineering quantities and low efficiency,and the methods based on depth learning have the problems of low extraction accuracy and poor connectivity in complex scenes.To solve the above problems and improve the accuracy of road extraction in different geomorphic regions,this paper proposes a road extraction method based on iHDODC LinkNet network for high-resolution remote sensing images.This method is improved on the basis of the semantic segmentation model D-LinkNet:on the one hand,ResNeSt50 is used to reconstruct the D-LinkNet network and a pre training model is added to propose a hybrid depthwise over-parameterized dilated convolution (HDODC) module.On the other hand,iterative attentional feature fusion (iAFF) mechanism is used to replace the original additive fusion,so that the model focuses on the global information of the road.Finally,the training is carried out on the Massachusetts road dataset and a provincial highway scene dataset,and the effectiveness of the improved model is proved by the extraction effect of the test set.According to the experimental model segmentation effect,the improved method applied to F1 reaches 71.66%,which is 10% higher than the original model,and better segmentation results can be obtained.