# Evaluating the Efficacy of Segment Anything Model for
## 4 Result
### Same Study Area Size at Different Resolutions  
The detailed image segmentation results are presented in Figures 7-12, showcasing the segmentation outcomes at resolutions of 0.5 m, 1 m, 3 m, 10 m, 20 m, and 30 m. Both conditional-based segmentation and unsupervised segmentation results are depicted individually. Table 4 provides the segmentation accuracy values for each image. Overall, SAM exhibits robust segmentation capabilities, regardless of whether it is applied to high-resolution or low-resolution ARSI and UGS images. As illustrated in Figure 13, we provide a visualization of all the SAE values. The accuracy of conditional-based image segmentation is consistently high, with the majority exceeding 0.9. In contrast, unsupervised classification methods exhibit sensitivity to image resolution, particularly underperforming on low-resolution images (3 m and above), where approximately 35% of the images experience a decline to 60% or even lower accuracy. It also suggests that resolution had less impact on conditional segmentation in SAM, whereas, for unsupervised segmentation, higher resolutions showed better segmentation accuracy. This observation indicates that, in practice, resolution has a relatively limited impact on the segmentation effectiveness of conditional segmentation in SAM. It is worth noting that unsupervised segmentation, while generally effective, has some segmentation results below 0.4, indicating its comparative limitations in certain scenarios. While SAM generally showcases robust segmentation capabilities, excelling in delineating cultivated areas with distinct boundaries and public green spaces within urban environments, it encounters challenges with certain image types, resulting in performance issues, such as overfitting. Typically, when annotating a substantial amount of contextual sample information for specific images, the segmentation effectiveness improves, as demonstrated in Graph 8 in Figure 10. However, some images exhibit a counterintuitive trend, where increasing annotations leads to a decrease in segmentation accuracy, as observed in Graph 6 in Figure 10, Graph 10 in Figure 8, and several others. These problematic images share a common issue--during the segmentation of the region of interest, they tend to distribute a significant number of extraneous elements, manifesting as sporadic features of other types. Alternatively, they may exhibit a distribution with closely related colors, as illustrated in Graph 4 in Figure 9. This underscores the limitations of SAM in handling more complex remote sensing images in certain segmentation scenarios. Figure 7: The 0.5 m resolution test results for different images. Graphs 1, 2, 3, 4, 6, 7, 8, and 10 in dataset A were utilized to extract general cropland areas, while Graph 5 was dedicated to extracting cropland areas in an urban context, and Graph 9 was employed to extract greenfield areas within the city. Different colors in the Unsupervised panel represent different segmentation polygons. Figure 8: The 1 m resolution test results for different images. Graphs 1, 2, 3, 4, 6, 7, and 8 in dataset B were utilized to extract general cropland areas, while Graphs 5 and 10 were dedicated to extracting public green areas in an urban context, and Graph 9 was dedicated to extracting cropland areas in an urban context. Different colors in the Unsupervised panel represent different segmentation polygons. Figure 9: The 3 m resolution test results for different images. Graphs 2, 3, 4, 5, 6, 7, 8, 9, and 10 in dataset C were utilized to extract general cropland areas, while Graph 1 was dedicated to extracting public green areas in an urban context. Different colors in the Unsupervised panel represent different segmentation polygons. **Figure 10.** The 10 m resolution test results for different images. Graphs 2, 3, 5, 7, and 8 in dataset D were utilized to extract general cropland areas, while Graphs 1 and 9 were dedicated to extracting public green areas in an urban context, and Graphs 6 and 10 were dedicated to extracting cropland areas in an urban context.