# A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters
## 4 Experiments
### Experiment Results
#### 4.4.2 Style Transfer Result on Google Maps Dataset  
We select urban and off-road environment datasets for group training. After completing the training, we input class-A-style data or class-B-style data into the model to generate the corresponding transformed image. In the following sections, we display and analyze the output results of the PSWA and SWAD methods. These results consist of 16 randomly selected outputs from over a thousand results obtained in urban and off-road environments. The relationship between the input and output is as follows: when the class-A-style test set is inputted, a class-B-style converted image is generated, and when the class-B-style test set is inputted, a class-A-style converted image is generated. Figures 9 and 10 illustrate the forward migration process using the PSWA method, while Figure 11 depicts the reverse migration process using the PSWA method.  
Figure 11: The reverse transfer process based on PSWA.  
Figure 10: The forward transfer process in off-road environments based on PSWA.  
Figure 12: The forward transfer process in urban environments based on SWAD.  
Figure 13: The forward transfer process in off-road environments based on SWAD.  
As observed in Figures 9 and 10, it is apparent that the forward migration effect of PSWA is not satisfactory. The map outline drawn appears blurred, and the migration effect of certain key elements is subpar. Overall, this can be attributed to the fact that although PSWA partially addresses the issue of over-fitting in ordinary SWA, it still lacks sufficient training, resulting in a decrease in the clarity of the generated images. Furthermore, the data adaptability to off-road environments is limited, and the mapping accuracy is low, which does not meet the requirements for hybrid mapping in multiple environments. Nevertheless, it is evident that PSWA exhibits a strong resemblance to the source domain, thereby demonstrating its potential to enhance the model's robustness.  
Figure 11 illustrates the reverse migration process of the PSWA method. The outlines of the buildings in the map appear to be clearer, although still somewhat blurry. The migration effect is more pronounced for data with significant differences in characteristics, but the distinction between the reverse generation of data with similar characteristics is low. Specifically, there is not a significant differentiation between rivers and green spaces in the data.  
From Figures 12 and 13, we can observe that the SWAD method effectively addresses the issue of over fitting without compromising the quality of generation. Furthermore, the clarity of the SWAD method surpasses the generation effect of the PSWA method. In urban environments, distinct features such as vegetation, lakes, and rivers are adequately represented in the remote sensing images and exhibit good transfer results. Boundaries like roads and sidewalks are also well reflected in the navigation maps. In off-road environments, the elements in the remote sensing images are relatively uniform, mainly consisting of deserts or channels. As a result, the transfer effect is good, essentially achieving the goal of generating hybrid-transfer maps between urban and off-road environments.  
When inputting class-B-style images, it should be possible to perform a reverse style transfer back to class-A-style images. Therefore, the results of the reverse transfer are shown in Figure 14. It can be observed that although some empty areas in the remote  
Figure 14: The reverse transfer process based on SWAD.  
sensing images can only be filled with houses or other objects generated by the algorithm after the transfer, the overall segmentation lines and block restoration are relatively good, resulting in a satisfactory outcome.