# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 6 Conclusions  
In this paper, we propose an end-to-end road segmentation network for road extraction tasks from remote sensing images. Although the CNN-based methods have greatly improved the accuracy of road extraction over traditional approaches, the road connectivity should be further improved to generate more complete results. As one of the important geometric topological properties, road connectivity is necessary for autonomous driving, vehicle navigation, and route planning. However, the existing CNN-based methods often fail to predict road connectivity and thus produce fragmented road segments. As a comparison, our proposed MSPNet is able to generate the road segmentation results with better connectivity and therefore meet the requirements of large-scale remote sensing data analysis. Specifically, the proposed MSPNet strengthens the linear feature of roads by introducing strip pooling layers, where its pooling kernel shapes are more in line with the roads. Accordingly, a multi-scale strip (MSP) module is developed to learn multiple long-range contextual information. In this paper, the widely used design of a symmetric encoder-decoder network with skip-connections is adopted to the low-level features to recover the spatial details, which is beneficial for parsing high-resolution remote sensing images. What is more, to alleviate the problem caused by unbalanced road and background pixels, we have performed ablation experiments to adjust the loss contributions between cross entropy and dice-coefficient loss functions to suit the task of road extraction. We have also compared the performance and computational cost of ResNet series models as the backbone network to select an appropriate backbone. Experimental results on a popular benchmark DeepGlobe dataset show the superiority of our proposed MSPNet compared with several mainstream methods. As discussed in the above section, some road types that have similar spectral and texture to background are not well identified, and there are still some discontinuities. Since road extraction can be viewed as a binary classification problem, it may be useful to suppress background noise to improve the generalization ability of the road segmentation model. This is left for our future studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **FLOPS (Gbps)** & **Interfence (s)** \\ \hline FCN & 97.47 & 0.097 \\ ResUNet & 191.36 & 0.145 \\ D-LinkNet & 84.51 & 0.123 \\ SE-Deeplab & 223.65 & 0.176 \\ MSPNet(ours) & 100.49 & 0.106 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: FLOPS and inference time of our proposed MSPNet and other methods. The inference times are calculated using 10 test images and then averaged. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation** & **IoU (\%)** & **F1 (\%)** \\ \hline \(\bigtimes\) & 73.26 & 84.07 \\ \(\bigvee\) & 73.64 & 84.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative study of data enhancement. \(\bigtimes\) and \(\bigvee\) denote our use and non-use of data augmentation strategies, respectively. **Author Contributions:** All authors made contributions to the manuscript. Conceptualization, methodology, software, validation and writing, S.L. and S.Q. ; software and visualization, S.Q. ; validation and investigation, B.Z. ; writing and preparation, S.Q. and H.Z. ; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially supported by the Science and Technology Development Plan Project of Henan Province, China (No. 212102210538,222102210101), Henan Key Science and Technology Project (No. 201300210400), Graduate Education Innovation and Quality Improvement Plan Project of Henan University (No. SYL20040121), National College Student Innovation and Entrepreneurship Training Program (No. 202110475135). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.