###### Abstract  
Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face challenges in capturing global context and accurately extracting the linear features of roads due to their localized receptive fields. To address these shortcomings of traditional methods, this paper proposes a novel parallel encoder architecture that integrates a CNN Encoder Module (CEM) with a Transformer Encoder Module (TEM). The integration combines the CEM's strength in local feature extraction with the TEM's ability to incorporate global context, achieving complementary advantages and overcoming limitations of both Transformers and CNNs. Furthermore, the architecture also includes a Linear Convolution Module (LCM), which uses linear convolutions tailored to the shape and distribution of roads. By capturing image features in four specific directions, the LCM significantly improves the model's ability to detect and represent global and linear road features. Experimental results demonstrate that our proposed method achieves substantial improvements on the German-Street Dataset and the Massachusetts Roads Dataset, increasing the Intersection over Union (IoU) of road class by at least 3% and the overall F1 score by at least 2%. s +
Footnote †: journal: Applied sciences  
article  
[color=gray!20, linecolor=gray! Road extraction is a specialized form of semantic segmentation that involves binary classification of roads versus background, which introduces unique challenges not typically encountered in general semantic segmentation. The binary nature of this task adds complexities despite the applicability of standard models. Advancing road extraction further requires overcoming three major challenges: complex environments impact extraction accuracy, subtle inter-class variance causes misclassification, and category imbalance limits model sensitivity. Complex urban and rural environments, as illustrated in Figure 11a, are frequently characterized by roads obscured by trees, buildings, or shadows, compromising continuity and topological integrity. Subtle inter-class variances, depicted in Figure 11b, cause urban roads to resemble rooftops and parking lots, while rural roads can look similar to exposed soil or farmland, increasing the likelihood of misclassification. Furthermore, the significant category imbalance shown in Figure 11c and quantified in Table 11 can lead models to overlook minority classes, reducing their sensitivity to roads and resulting in poorer performance in road extraction. Research on road extraction methods has advanced significantly, with various algorithms utilizing diverse techniques for road recognition and optimization. Consequently, road extraction from remote sensing images can be broadly classified into three categories: early-generation methods, CNN-based methods, and Transformer-based methods. Early-generation methods relied on traditional image processing and basic feature extraction techniques. These approaches identified roads using fundamental visual characteristics such as color, shape, edges, and texture, employing mathematical and morphological algorithms. [PERSON] et al. [7] combined the Snake model with a median filter to reduce noise while preserving edges, iteratively generating curves to represent road contours. [PERSON] et al. [8] applied level set methods and fuzzy inference to extract road networks from low-quality satellite images. While adaptable to complex scenes, these methods are computationally intensive and sensitive to parameter settings and initial conditions. [PERSON] et al. [9] introduced Boltzmann machines (RBMs) for road segmentation in high-resolution images, pioneering deep learning in this field. CNNs later improved practicality by reducing manual tuning, computational complexity, and enhancing generalization.