# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 2 Related Work
### CNN-Based Methods  
_(1) Segmentation of Roads:_ Semantic segmentation is a basic and essential research domain in computer vision. With the great success of deep learning in the field of semantic segmentation, some studies [3; 16; 17] consider the road extraction as a binary semantic segmentation problem using CNN-based approaches. [PERSON] et al. [18] firstly present a neural network-based approach with restricted Boltzmann machine (RBM) for detecting roads in high-resolution aerial images. Some deep learning models with encoder-decoder structures such as UNet [19] and LinkNet [20] have been proven to be efficient in the field of semantic segmentation, and their variants have also been widely proposed to segment roads [8; 10; 21]. A semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction by [PERSON] et al. [22]. [PERSON] et al. [23] follow the LinkNet architecture and employ dilated convolution layers with both cascade mode and parallel mode to enlarge the receptive field. [PERSON] et al. [24] propose an HsgNet, which inserts a Middle Block based on bilinear pooling into the middle of LinkNet between the encoder and decoder. These methods usually perform better compared with traditional methods, but they cannot guarantee the connectivity of roads and thus produce fragmented road segments.  
_(2) Connectivity of Roads:_ Recently, several works are proposed to obtain segmentation results with better road connectivity. [PERSON] et al. [25] put forward a road extraction method based on a LinkNet deep learning model, and at the pre-processing step, an auxiliary constraint task is designed to solve the connectivity problem caused by occlusions. [PERSON] et al. [21] propose a fusion network (FuNet) with the fusion of remote sensing imagery and location data, and a universal iteration reinforcement (IteR) module is added to enhance the ability of road connectivity reasoning. Meanwhile, [PERSON] et al. [26] introduce a deep learning-based multistage framework to extract the road surface and road centerline simultaneously. They initially segment roads with an FCN-based model, after which an iterative search strategy is applied to track consecutive and complete road networks. However, the iterative steps are time-consuming. The authors of [6] employ a novel linearity index for the discrimination of elongated road segments from other objects and customized tensor voting, which is utilized to fill missing parts of the road network. In these approaches, pre-processing or post-processing is added to maintain the connectivity of roads. However, they are time consuming and not suitable for some areas with high road density and occlusions.  
In conclusion, although the CNN-based methods have greatly improved the accuracy of road extraction, most of them are simple extensions of the widely used CNN architectures and do not consider the structural features of roads. Therefore, there are still margins to improve the accuracy of road extraction in terms of topological connectivity.