## 4 Experiments
### Qualitative Experimental Results  
Figure 4 shows the extraction results of the seven network models on the test image set, and the segmented images in seven different scenes are selected for analysis, namely Scenes 1 to 7 from left to right. Comparing the overall classification effects, no matter if one is comparing with the labeled samples or comparing with the actual open-pit mining area on the image, all seven models present different degrees of failure to recognize the mining area as well as incorrect recognition of the mining area. Among them, relative to the actual mining area, Segmenter fails to recognize the mining area in most cases, with obvious errors such as failing to recognize the small mining area in Scene 1 and failing to correctly recognize parts of the mining area in Scene 2, Scene 3, Scene 5, and Scene 7. RtFormer fails to identify the small mining area in Scene 1, identifies the small, vegetation-covered area in Scene 3 as a mining area, fails to correctly identify a portion of the mining area in Scene 4, and identifies a small portion of the non-mining area in Scene 7 as a mining area. SegFormer identifies the large vegetation-covered area in Scene 3 as a mining area, fails to identify the small mining area in the center, and identifies the large non-mining area in Scene 7 as a mining area. TopFormer fails to correctly identify the mining area in Scene 2 and Scene 3 on the one hand and identifies the large non-mining area of Scene 7 as mining area on the other hand. Mask2 Former fails to accurately identify the large mining area in Scene 3 and Scene 7. ViT-Adapter generally fails to correctly identify the mining area, e.g., completely fails to identify the mining area in Scene 1 and Scene 2 and fails to recognize the large-scale mining area in Scene 7, but its recognition results for Scene 4 are the best among the seven models. Overall, Segmenter has poor adaptability to multi-scale targets, resulting in the model not recognizing small open-pit mining areas. RtFormer can only output a fixed-resolution feature map, and it is difficult to realize the segmentation of fine boundaries when the image resolution is not too high. TopFormer performs feature enhancement at 64 times the downsampling resolution, which is prone to losing global information and has poor segmentation accuracy for large-scaled mining areas. Mask2 Former is unable to obtain complete semantic information when facing complex images, and accurate segmentation fragments are not effectively combined into a complete and coherent object. ViT-Adapter has limited ability to extract and integrate local features to form a global segmentation profile, resulting in the inability to accurately capture the whole shape of the object. SegMine improves the model's ability to segment small-scale targets by supplementing multi-scale local spatial features and uses an attention mask decoder to integrate semantic information and reduce pixel classification errors. In Scenes 1 and 5, the open-pit mining areas are small, and the distribution is scattered, yet SegMine effectively retains the detailed information of these targets compared to other models. In Scenes 2, 3, and 6, SegMine utilizes semantic information to generate segmentation masks, which provides clearer target contour boundaries. In Scene 7, the open-pit mining area has a complex environment with obvious terrain undulations, broken and mixed multi-type land cover, fragmented distribution of vegetation, and obvious shadows, which makes it difficult to achieve a clear segmentation of the target contour boundary. However, among all the models, the SegMine segmentation effect performs better and has the ability to deal with the complex open-pit mining area scene. The previous section mainly compares and analyzes the consistency of the extraction results of the seven models with the label results. In the following section, we continue to analyze the degree of consistency between the SegMine extraction results and the actual environment.