# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 4 Results and Discussion  
### Results on the Processing Datesets  
The processing dataset is divided into simple, general and complex scenes, following the position of the image contents. The three-level samples are shown in Figures 9-11, respectively.  
The road areas are highlighted in the simple scenes, and only a tiny part of the road areas is covered by shadows. The road areas around the building groups and some parts of the road areas are covered by buildings, trees and shadows in the general scenes. Greenhouses and buildings have similar features to the road, which will affect road area extraction. Large parts of the road are sheltered by trees and shadows in the complex scenes, which increases the difficulty of road extraction. The objective of these experiments is to obtain the model size and IoU of each network with different layers. Then, we can determine whether the proposed network is effective for precision (IoU) and computing power consumption (model size).  
All networks selected in this research have effective performance for road extraction in the simple scenes. The road areas are extracted precisely and almost closed to the labels. We selected four networks to extract roads from satellite images. In the experiment, the number of layers for D-LinkNet is 101, while, for other networks, the number of layers is set as 24. In the simple scenes, the difference is not obvious. For Figure 10, all networks did not recognize the highlighted greenhouses or highlighted buildings as road areas. However, there are several road interruptions because of the shelters, especially in the third-row images. Only D-LinkNet (101) can extract the road areas covered by trees. In contrast to the fourth-column results and the fifth-column results in Figure 11, we find that the effect of the stem block is better prediction than that of the initial block. In addition, long distance road areas can also connect according to the context, while D-DenseNet (k = 24) + stem block is the best.  
Figure 9: Results in the simple scenes: (**a**) original images; (**b**) labels; (**c**) D-LinkNet (101); (**d**) DenseNet (k = 24) + initial block; (**e**) DenseNet (k = 24) + stemblock; (**f**) D-DenseNet (k = 24) + stem block.  
In terms of the improvement of road extraction accuracy for the improved network, various networks with D-Block are shown in Figure 7. The model size and IoU score of different networks are shown in Table 1. D-LinkNet (k = m) indicates that the selected network is constucted with a residual block and an encoder path has m convolutional layers, DenseNet (k = m) indicates that the network was constructed with the dense block and the growth_rate is m, and D-DenseNet (k = m) indicates that the network was constructed with the D-Dense block and the growth_rate is m. These networks built on the dense block have a smaller model size and a higher IoU score than those with a residual block. Further, the networks with a stem block have a smaller model size and a higher IoU score than that of the networks with an initial block. A smaller model scale can make the computation more efficient. The results show that D-DenseNet shows better performance with a similar model size.  
Figure 11: Results in the complex scenes: (**a**) original images; (**b**) labels; (**c**) D-LinkNet (101); (**d**) DenseNet (k = 24) + initial block; (**e**) DenseNet (k = 24) + stem block; (**f**) D-DenseNet (k = 24) + stem block.  
Figure 10: Results in the general scenes: (**a**) original images; (**b**) labels; (**c**) D-LinkNet (101); (**d**) DenseNet (k = 24) + initial block; (**e**) DenseNet (k = 24) + stem block; (**f**) D-DenseNet (k = 24) + stem block.