Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction from remote sensing images. CNN-based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset, which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this article first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different upsamplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

With the advancement of 3D sensing technologies, point clouds are gradually becoming the main type of data representation in applications such as autonomous driving, robotics, and augmented reality. Nevertheless, the irregularity inherent in point clouds presents numerous challenges for traditional deep learning frameworks. Graph neural networks (GNNs) have demonstrated their tremendous potential in processing graph-structured data and are widely applied in various domains including social media data analysis, molecular structure calculation, and computer vision. GNNs, with their capability to handle non-Euclidean data, offer a novel approach for addressing these challenges. Additionally, drawing inspiration from the achievements of transformers in natural language processing, graph transformers have propelled models towards global awareness, overcoming the limitations of local aggregation mechanisms inherent in early GNN architectures. This paper provides a comprehensive review of GNNs and graph-based methods in point cloud applications, adopting a task-oriented perspective to analyze this field. We categorize GNN methods for point clouds based on fundamental tasks, such as segmentation, classification, object detection, registration, and other related tasks. For each category, we summarize the existing mainstream methods, conduct a comprehensive analysis of their performance on various datasets, and discuss the development trends and future prospects of graph-based methods.