# Evaluating the Efficacy of Segment Anything Model for
## 4 Result
### Same Resolution with Different Study Area Size  
The results for different image sizes are presented in Figures 17-22, showcasing segmentation outcomes for images in large, medium, and small scales. The segmentation accuracy values for each image can be found in Table 5. For ease of interpretation, we have visually represented the results from Table 5 by assigning different colors to the SAE values associated with various study area sizes, as depicted in Figures 23 and 24. A clear pattern emerges from the visualization: larger image sizes tend to result in poorer segmentation outcomes. Conversely, for small-sized images, regardless of resolution, the SAE values consistently hover around 0.8. This reaffirms that image size significantly influences the segmentation performance of SAM, with smaller frame-sized images generally yielding more accurate segmentations. In the context of unsupervised segmentation, SAM exhibits limitations across various image sizes, particularly struggling with low-resolution images where the accuracy sharply decreases. Conversely, SAM tends to achieve higher accuracy when processing smaller-sized high-resolution images. However, challenges arise with larger-sized high-resolution images, as seen in Graphs 6 and 8 in Figure 17, where extracted objects in urban areas appear more fragmented. Larger map sizes, indicative of increased feature information, pose challenges for SAM, especially in segmenting scattered urban green spaces. Shifting to conditional segmentation, larger images generally display comparatively poorer performance, especially with decreased image resolution, resulting in significant fluctuations in accuracy. Notably, for small-sized image segmentation, some images exhibit increased accuracy with additional training samples, illustrated in Graph 2 in Figure 21. However, this improvement is not as pronounced for large-sized images of the same style, as shown in Graph 5 in Figure 21 and Graph 9 in Figure 22. The abundance of information significantly limits SAM's segmentation ability, even with the addition of  
Figure 16: Statistical graph of test results of image segmentation with different resolutions at the same location (unsupervised). more training samples. For large-sized images, SAM-based segmentation achieves better performance even without more obvious boundaries. Feature texture differences play a crucial role in segmentation success, even when colors are closer, as demonstrated in Graph 9 in Figure 18 and in Graphs 4 and 6 in Figure 22. Nevertheless, clearer boundaries generally result in improved segmentation outcomes. In terms of segmentation types, urban public green spaces, and peri-urban cultivated land exhibit strong adaptive capabilities, showcasing effective segmentation in both large-scale and small-scale images. Their common characteristic is the presence of distinct boundaries within the urban area and unique texture characteristics. Conversely, common arable land and urban green belt areas show poorer segmentation performance. Common arable land, however, demonstrates higher segmentation effectiveness in high-resolution images (0.5 m and 1 m), delivering excellent results across various image sizes. For low-resolution images (3 m and above), the segmentation effectiveness improves for smaller-sized images and continues to enhance with an increased number of sample points. However, in medium-size and large-size images, disorder phenomena become prominent, leading to a segmentation preference for object-oriented methods. This tendency hampers the segmentation of finer and more scattered distributions of cultivated areas. The segmentation performance of SAM on urban green zones in high-resolution images relies on the distribution of features in the photographs. Generally, the performance is better in low-resolution photographs, and as the size of the photographs increases, SAM's segmentation effect surpasses that of medium-size images. However, with increasing size and variety of land use types, the segmentation effect fluctuates.