In road extraction from remote sensing images, the road environment is complex and blocked by trees, buildings, and other objects, making it impossible to extract practical (continuous and complete) road information. We propose a joint attention encoder–decoder network (JAED-Net) for road extraction from remote sensing images to solve these problems. First, JAED-Net encodes a modified residual network as the backbone for road feature extraction. A joint attention module is added to the encoder to enhance the network’s ability to learn and express road features. Then, strip convolution is added to the decoder, so the network retains more spatial features, such as the width and connectivity of roads during upsampling. Finally, a hybrid weighted loss function is introduced to train the network and ensure stability because of the unbalanced ratio of road and background pixels in remote sensing images. Experimental validation of the proposed network is performed on three publicly available datasets.