# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 1 Introduction  
Remote-sensing technology is continuously advancing, resulting in improved quality of remote-sensing images. These images provide valuable information for various applications, such as urban planning [1], vehicle navigation [2], intelligent transportation [3], and land use monitoring [4]. In particular, road information in remote-sensing images is crucial for disaster prevention and mitigation [5], unmanned vehicle path planning [6], and other related fields. The segmentation results of remote-sensing images present additional issues, including incorrect recognition of background and foreground elements and jaggedness at the edges of segmentation targets. Particularly in complex urban environments, it is common for the algorithm to mistake background information for road information. When the semantic segmentation results of remote-sensing images display errors in recognizing background and foreground, it is mainly due to the large-scale variations in the middle scale and the high variance within the background class of the remote-sensing images. Additionally, the foreground-background ratio is extremely unbalanced. To overcome this issue, research scholars have proposed the foreground-aware relational network (FarSeg) [7], which takes into account the foreground class during segmentation. This study focuses on enhancing foreground features by understanding the symbiotic relationship between the scene and foreground. To correlate contextual information related to the foreground, scholars have used dynamic convolutional layers to build Gaussian dynamic convolutional networks. They have also proposed Gaussian pyramid pooling (GPP) [8] to perform target semantic segmentation for multi-scale targets. The GPP structure has proven to be more efficient in aggregating contextual information in experiments, particularly in enhancing the understanding of remote-sensing images. The attention mechanism used for processing semantic segmentation in natural images (as discussed in references) [9; 10; 11; 12] is also applicable in the semantic segmentation of remote-sensing images. The network architecture of the SENet [13] is an improved version of the classical attentionnetwork. It includes the squeeze-and-excitation (SE) block, which is a channel attention mechanism that enhances the representation quality of the convolutional neural network (CNN) by establishing interdependence between feature map channels. Researchers have proposed a dual attention mechanism [14] that employs both location and channel attention modules to learn the spatial and channel interdependence of features. This helps establish rich contextual dependencies, which in turn improves semantic segmentation performance. However, some network structures tend to acquire dense contextual information at the cost of introducing a large number of parameters, leading to excessive memory usage. CCNet [15], proposed by some scholars, utilizes the criss-cross attention module as its core. However, as it is designed for fixed-size input images, it may need extra processing to accommodate various input sizes in certain application scenarios. Remote-sensing images often contain a vast number of thin, narrow, and complex roads, which pose a challenge for semantic segmentation. These roads are difficult to distinguish from the background, leading to confusion between foreground and background. This problem is particularly acute in urban areas with complex road networks, where the algorithm may mistakenly classify background as road information. To solve this problem, it is recommended to use feature pyramid pooling. While some researchers suggest using SPP (spatial pyramid pooling) [16] structure or PSPNet (pyramid scene-parsing network) [17] to combine local and global information, these methods tend to rely too heavily on deep features and not enough on shallow features, resulting in poor detail segmentation. On the other hand, the DeepLab [18; 19; 20; 21] series network uses atrous spatial pyramid pooling (ASPP) to achieve better results.