## 4 Experiments
### Qualitative Experimental Results
It is reasonable to say that accurate labeling is a prerequisite for completing model training with high accuracy and is also the basis for fine-grained prediction in the later stage. However, on the one hand, the actual environment of mining areas is very complex, and usually there are different types of land cover with very different textural characteristics. Especially when there are fuzzy boundaries, it is sometimes very difficult to accurately label the mining area by relying on remote sensing images alone. Even in the same region, the labeling results of two professional technicians will never be exactly the same, and in extreme cases, they may be very different. On the other hand, to a largeextent, current sample labeling usually relies on manual work, and high-precision labeling often implies a large amount of manpower input, thus greatly increasing the production cost, which is often unaffordable for specific applications. Therefore, it is impractical and undesirable to build an absolutely accurate sample database for practical applications. The optimal solution should be to let the model absorb the correct knowledge in the imperfect sample and reject the wrong knowledge through continuous learning. The sample database constructed in this study also has many problems. Unsatisfactorily, in Figure 4, some regions are not accurately labeled. When the samples are not rich enough, to a certain extent, there will be conflicted labeling, which can mislead the model training and lead to large differences between prediction and samples. In turn, this leads to inconsistency with the reality. However, in general, among the seven models, the extraction results of SegMine are the closest to the reality and have obvious advantages. Figure 4: Segmentation of seven models on the open-pit mining area dataset. In the following section, we will analyze the extraction results of SegMine for each of the seven scenes. Each scene consists of three images, which, from left to right, are the original image, the labeled image, and the original image with overlayed extraction results. In the labeled image, the white area is the open-pit mining area, and the black area is the background. In the overlayed image, the brown area is the open-pit mining area, and the green area is the background. As can be seen in the original image of Scene 1, the upper portion has a small river running from left to right and turning right at 90 degrees downhill in the upper right corner. There is a small area of open-pit mining on the upper-right riverbank. Compared to the original image, the mining area in the labeled image is larger than the actual area, and some parts of the exposed riverbed are also labeled as mining area. In terms of spectral and textural features, the results extracted by SegMine, however, do not recognize the bare riverbed as a mining area, which is more in line with the actual situation. In Scene 2, there is a small area of mining in the upper part. Compared to the original image, the environment of the mining area is complex, and the lack of green vegetation due to the season causes the boundary of the mining area not to be clear enough. The labeled extent of the mining area in the sample image is inaccurate in local areas, and the technician did not draw along the boundary of the mining area well. In terms of spectral and textural features, the results extracted by SegMine are more in line with the actual conditions; especially in the obvious boundary areas, the extracted boundaries match the actual boundaries very well. In Scene 3, there is extensive open-pit mining on the left side. Compared to the original image, the boundaries of this mining area are relatively clear, but the extent of the mining area labeled in the sample is not accurate. The two problems of over-extraction (e.g., two small areas covered by vegetation in the top-left and -right) and under-extraction (e.g., strip mining area in the middle-right) coexist.