## 3 Methods
### Creation of the Supplemented Mask and the Modified Mask Using Redundant Models
In the enhanced model, we adjusted the loss by weighting it according to the probability of making the correct decision (P) using the confusion levels in the class mask (Equation (4). This approach ensures that for pixels with higher confidence, the parameter updates are stronger, whereas for pixels with greater ambiguity, the updates are less impactful. \[\mathrm{L}\left(\widehat{M},M\right)=\sum_{x}\mathrm{P}(x)\cdot\mathrm{L} \left(\widehat{M}(x),M(x)\right) \tag{4}\]  
where \(x\) is the pixel location. If the probability of making the correct decision (P) is zero, the parameters of the enhanced model will not be updated. In these situations, redundant models predict a different class, which we refer to as the dominant class, instead of the GT class. If the probability of the class surpasses that of the GT class, it suggests that the GT may be incorrect. In this case, we can replace the GT class with the dominant class, and P is adjusted based on the difference in probability between the dominant class and the original GT class. This results in the modified dataset with which the consensus model is trained. If the dominant class's probability is only slightly higher than that of the GT, the GT will be replaced, but with a very low confidence level. This means that it will have a minimal effect on the parameter updates while training the consensus model. For the experiment, the enhanced and consensus models used the same training and validation combination as the raw model, the first redundant model. Figure 9: The effect of varying the number of redundant models on the confusion levels: (**a**) the image, (**b**) the original GT mask, and (**c**-**f**) the supplemented masks when M is 8, 12, 16, and 20. Figure 10 shows a comparison of three types of masks. The supplemented mask identifies the boundaries between different classes as confusing areas, while the modified mask replaces those areas with the background, resulting in greater precision. In the multi-dataset scenario, the main distinction is that the redundant models trained on the host dataset are also utilized to assess the confusion level of the guest dataset. An intrinsic difference between the two datasets is that the guest dataset lacks the barren class. We anticipate that the intervention of the redundant models will help address this anomaly. Figure 11 illustrates examples from the guest dataset, comparing three types of masks with the corresponding images. The first and second samples show that the barren areas designated as background in the original mask are identified as confusing in the supplemented mask and are substituted with the barren class in the modified mask. The third and fourth samples indicate that the missing forest areas are marked as confusing in the supplementary mask and are replaced by the forest class in the modified mask. Additionally, the modified mask distorted some building boundaries and eliminated certain roads. This suggests that the host dataset has lower-quality annotations for these classes, which may lead to degraded performance in the consensus model. Figure 11: _Cont._  
Figure 10: Comparison of three types of masks: (**a**) the image, (**b**) the original mask, (**c**) the supplemented mask, and (**d**) the modified mask.