# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 1 Introduction
In V2 and later versions, the ASPP structure has been adjusted to extract multi-scale semantic information and improve feature fusion, ultimately leading to higher accuracy in semantic segmentation. The most commonly used network structure for semantic segmentation is the code-decode structure, with the U-Net [22; 23] series being the classic example. However, U-Net is not the best option when it comes to segmenting object boundaries. On the other hand, U-Net++ is capable of capturing features at different levels, but it does come with the downside of introducing more parameters, which results in larger memory usage. SegNet [24] is characterized by not requiring the storage of the feature map of the entire encoder stage but only the pooling index, resulting in memory space savings, and the code-decode structure is still applicable in the field of semantic segmentation of remote-sensing images. The win-transformer backbone has been introduced by some scholars to extract contextual information. They have used a new decoder called a densely connected feature aggregation module (DCFAM) [25] to recover the resolution and generate segmentation maps. The DCFAM network structure is more adaptable to large-size images and has a faster computation speed. However, it may not be as effective in detecting small targets. The DPPNet [26] model utilizes a deep pyramid pool (DPP) module and a dense block of deep multiple unfolding remaining connections to effectively leverage the pyramid structure and multi-level features in prediction. However, it may not be as effective in handling complex road information in remote-sensing images. In the field of remote sensing road semantic segmentation, researchers have introduced a novel fusion network called FuNet (fusion network) [27] that combines remote-sensing images and datasets to improve the network's learning ability. One notable feature of this network is the inclusion of an iterative reinforcement module. However, this approach has the drawback of potentially losing global information, resulting in discontinuous and jagged boundaries in the predicted roads. In this paper, we propose a remote-sensing image road semantic segmentation network called DANet. The network is based on a dual-ASPP structure, which addresses certain issues in semantic segmentation and incorporates various methods to improve the accuracy of the segmentation. The network uses two ASPP structures to fully extract semantic information at different scales in the road, which is crucial for accurate pixel classification. In this paper, we employ the DarkNet network structure, as used in YOLOv3 [28], for downsampling. Additionally, we utilize the SegNet network for upsampling, which is a classical codec network structure. The contributions of this paper are outlined as follows:1. This paper proposes a novel code-and-decode network structure that combines an optimized DarkNet network structure for downsampling with a SegNet partial network for upsampling. 2. The ASPP structure is utilized twice in feature extraction to enhance the extraction of both road detail features and global features. 3. The proposed network structure is evaluated on the CHN6-CUG Roads Dataset [29], where several advanced semantic segmentation methods are compared and analyzed to verify the effectiveness and advancement of the proposed network structure with and without ASPP.