## 1 Introduction
Despite its simple structure, U-Net [12] remains a popular choice for aerial image segmentation tasks [20; 21; 22]. It is not easy to conclude that more complex models yield more reliable results in remote sensing applications. This may stem from limitations in ground resolution and the challenges associated with creating perfect ground truth data. Additionally, more complex networks often risk overfitting the errors present in the dataset [23]. Even in the absence of human error, it is common for different class areas to appear similar, making them difficult to distinguish through human observation. Therefore, we need a method to address these human and intrinsic errors [24] in the GT. There is another trend that aims to enhance the robustness of models without requiring the creation of new datasets. The first approach is multi-model ensemble. Fusing the inference results of multiple models can produce more robust results than more complex networks, and the performance advantage of multi-model ensembles has been well verified through various datasets [25]. The second approach is to improve performance by supplementing GT information. [PERSON] et al. demonstrated that performance is enhanced by computing training loss by assigning pixel weights according to their relative importance based on class distribution and geometric uncertainty [26]. [PERSON] et al. demonstrated that the virtual labels generated by a model trained on the original dataset using Gaofen-2 images can enhance the robustness across images from various sensors, including Gaofen-1, Jinlin-1, Ziyuan-3, Sentinel-2A, and Google Earth images [27]. This can also be viewed as a performance improvement due to the supplementation of GT information. Ensemble methods offer a promising approach for enhancing model robustness. A classic example of this is the Random Forest algorithm, which utilizes multiple decision trees to produce more reliable outcomes. This method has the advantage of reasonable run-time inference since the computational complexity of individual decision trees is relatively low. However, recent classifiers based on deep neural networks exhibit enormous computational complexity, making it challenging to implement an ensemble consisting of a large number of models during run-time. To address this issue, we aimed to create a novel approach that consolidates the collective intelligence of ensemble methods into a single dataset. This allows us to train one model that incorporates the strengths of the ensemble, clearly demonstrating its impact through pixel-level confidence levels. It uses redundant models trained on various combinations of training and validation sets to enhance the original datasets. The confidence level estimated by the redundant models for each pixel is incorporated into the original GT mask and is reflected during the loss calculation of the enhanced model. The supplemented mask shows indistinguishable regions due to inherent ambiguities caused by human error or lack of resolution. We introduced one more revised mask, the modified mask, which is generated by replacing the low-confidence GT class with the dominant class voted by the majority of redundant models. Additionally, we applied the method to fuse heterogeneous datasets from different image sources, which have slightly different class compositions.