# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 4 Discussion  
In this investigation, we assessed the efficacy of the HRU-Net model for road extraction from high-resolution remote sensing imagery. The experimental findings lend credence to our supposition that the HRU-Net model can proficiently delineate road information from these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Parameters (M)** & **FLOPS (GLOPS)** \\ \hline U-Net & 29.95 & 5.64 \\ ResNet & 25.56 & 5.40 \\ Deeplabv3 & 5.87 & 6.61 \\ ResUnet & 38.52 & 8.64 \\ HRNet & 28.53 & 4.66 \\ \hline
**HRU-Net (ours)** & **32.78** & **6.06** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of computational efficiency of different methods.  
Figure 8: The extraction results of different experimental schemes on the DeepGlobe Road Extraction Dataset.  
types of images. When appraised on the metrics of precision, recall, and intersection over union (IoU), the HRU-Net model manifested a superior performance in comparison to other cutting-edge methodologies employed for road extraction from remote sensing imagery, such as U-Net, ResNet, DeepLabV3, ResUnet, and HRNet [36, 37, 38]. These comparative findings portray the HRU-Net model as a promising candidate for implementing road extraction from high-resolution remote sensing images. These results bear significant practical implications. The competency to precisely delineate road information from high-resolution imagery can influence a gamut of domains including urban planning, traffic management, and disaster response [39]. The adeptness of the HRU-Net model could potentially transform these domains by providing higher precision and detail in road information.  
Notwithstanding, our study was constrained by certain limitations. The precision of the HRU-Net model was compromised in certain intricate scenarios, as evidenced by blurred edges, breaks, or omissions when the MRF module was omitted. Likewise, the continuity of the road was disrupted, leading to interruptions or incomplete road segmentation when the UMR module was excluded. These constraints imply that while the HRU-Net model demonstrates promise, it requires further refinement to enhance its performance in intricate scenarios.  
Interestingly, our observations indicate that the quantity of network modules had a consequential effect on the model's performance. The incorporation of the UMR and MRF modules into the HRU-Net model played an instrumental role in boosting the performance of the road extraction endeavor. Their design principles and functionalities acted in unison, thereby augmenting the model's perceptual capabilities, feature expression competency, and precision. Nevertheless, the escalation in the number of modules also culminated in an increase in computational complexity, which could potentially impact the model's efficiency.  
For future investigations, we advocate for a deeper exploration into the UMR and MRF modules. Our findings denote that these modules contribute significantly to the enhancement of the road extraction task's performance. Subsequent research could focus on optimizing these modules to further ameliorate the model's performance in intricate scenarios, while also keeping a check on the equilibrium between performance augmentation and computational efficiency.  
To summarize, our study provides substantial evidence that the HRU-Net model is a potent tool for road extraction from high-resolution remote sensing images. Despite the presence of certain limitations, the model demonstrated superior performance in comparison to other advanced methodologies in our experiments. The UMR and MRF modules, in particular, were critical in augmenting the model's performance. These findings not only contribute to the discipline of remote sensing image analysis but also lay the groundwork for future investigations.