Road extraction from satellite imagery is vital in a broad range of applications. However, extracting complete roads is challenging due to road occlusions caused by the surroundings. This paper proposed an improved encoder-decoder network via extracting road context and integrating full-stage features from satellite imagery, dubbed as RCFSNet. A multi-scale context extraction (MSCE) module is designed to enhance the inference capabilities by introducing adequate road context. Multiple full-stage feature fusion (FSFF) modules in the skip connection are devised to provide accurate road structure information, and we devise a coordinate dual attention mechanism (CDAM) to strengthen the representation of road features. Extensive experiments are carried out on two public datasets, as a result, our RCFSNet outperforms other state-of-the-art methods. The results indicate that the road labels extracted by our method have preferable connectivity. The source code will be available at https://github.com/CVer-Yang/RCFSNet.

Road extraction is a significant research hotspot in the area of remote sensing images. Extracting an accurate road network from remote sensing images is still challenging, because some objects in the images are similar to the road, and some results are discontinuous due to the occlusion. Recently, convolutional neural networks (CNNs) have shown their power in a road extraction process. However, the contextual information cannot be captured effectively by those CNNs. Based on CNNs, combining with high-level semantic features and foreground contextual information (FCI), a novel road extraction method for remote sensing images is proposed in this letter. First, the position attention (PA) mechanism is designed to enhance the expression ability for the road feature. Then, the contextual information extraction module (CIEM) is constructed to capture the road contextual information in the images. At last, an FCI supplement module (FCISM) is proposed to provide foreground context information at different stages of the decoder, which can improve the inference ability for the occluded area. Extensive experiments on the DeepGlobal road dataset showed that the proposed method outperforms the existing methods in accuracy, intersection over union (IoU), precision, and $F1$ score and yields competitive recall results, which demonstrated the efficiency of the new model.

Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sensing images are densely distributed with a very small proportion of road information against a complex background, significantly impacting the integrity and connectivity of the extracted road network structure. To address this issue, we propose a method named StripUnet for dense road extraction from remote sensing images. The designed Strip Attention Learning Module (SALM) enables the model to focus on strip-shaped roads; the designed Multi-Scale Feature Fusion Module (MSFF) is used for extracting global and contextual information from deep feature maps; the designed Strip Feature Enhancement Module (SFEM) enhances the strip features in feature maps transmitted through skip connections; and the designed Multi-Scale Snake Decoder (MSSD) utilizes dynamic snake convolution to aid the model in better reconstructing roads. The designed model is tested on the public datasets DeepGlobe and Massachusetts, achieving F1 scores of 83.75% and 80.65%, and IoUs of 73.04% and 67.96%, respectively. Compared to the latest state-of-the-art models, F1 scores improve by 1.07% and 1.11%, and IoUs increase by 1.28% and 1.07%, respectively. Experiments demonstrate that StripUnet is highly effective in dense road network extraction.

Remote sensing images contain many objects that resemble road structures, making it diffcult to distinguish roads from the background. Moreover, road extraction is affected by many factors, such as lighting conditions, noise, occlusions, etc., resulting in incomplete and discontinuous road extraction. Learning discriminative road features from remote sensing images is a highly challenging task. In this paper, a novel road extraction model is proposed for remote sensing images under encoder and decoder U-Net like architecture. An axial Transformer module (ATM) is designed to learn global road features in the deepest layer with linear computational complexity regarding image size. And a multilayer attention fusion module (MLAF) is also presented to fuse multiple layers of Transformer features, obtaining more comprehensive and richer semantic information. In the skip connection, a channel attention module (CAM) is designed to weight the feature maps along the channel dimension, with the goal of improving the capability of feature representation. Extensive experiments are conducted on the DeepGlobe and Massachusetts road datasets. Compared with other methods, our proposed method in this paper realized road extraction from remote sensing images with higher accuracy and less computational cost, e.g., achieving a intersection over union (IoU) of 81.71% (1.02% improvement) and a 22.38% reduction in convergence time over the latest TransRoadNet on the Massachusetts road dataset. Ablation experiments also demonstrate the effectiveness of the designed model.