Although the existing U-Net provides an ideal solution for remote sensing road extraction, its lack of attention to global information leads to the model's insufficient ability to extract contextual information. In order to further improve the accuracy and completeness of road extraction, context＆multilayer features-UNet(CMF-UNet), which utilizes a pyramid feature aggregation module to fuse multi-layer features and introduces a multi-scale contextual information extraction module to enhance the contextual information capture capability, is proposed. Experimental validation is conducted on two datasets, Massachusetts Roads and CHN6-CUG, and the results show that compared with U-Net, CMF-UNet improves recall, F1-score, and intersection over union on the Massachusetts Roads dataset by 5.77 percentage points, 2.02 percentage points, and 2.62 percentage points respectively; on the CHN6-CUG dataset, recall, F1-score, and intersection over union are improved 6.47 percentage points, 1.53 percentage points, and 2.04 percentage points, respectively.