# Evaluating the Efficacy of Segment Anything Model for
## 4 Result
### Same Study Area Size at Different Resolutions
Different colors in the Unsupervised panel represent different segmentation polygons. Figure 12: The 30 m resolution test results for different images. Graphs 1, 2, 3, 4, 6, and 10 in dataset F were utilized to extract general cropland areas, while Graphs 5, 7, 8, and 9 were dedicated to extracting public green areas in an urban context. Different colors in the Unsupervised panel represent different segmentation polygons. \begin{table}
\begin{tabular}{c c c c} \hline
**Resolution** & **Serial Number** & **Unsupervised (SAE)** & **Conditional (SAE)** \\ \hline \multirow{8}{*}{0.5 m} & A\_1 & 0.84 & 0.92 \\  & A\_2 & 0.94 & 0.96 \\  & A\_3 & 0.92 & 0.96 \\  & A\_4 & 0.90 & 0.92 \\  & A\_5 &  
In terms of segmentation types, peri-urban cropland, common cropland, and urban public green spaces consistently exhibited suitable classification accuracy across various resolutions. Extensive testing of cultivated fields in different scenarios yielded positive results attributed to the inherent texture and color uniformity of cultivated areas. Similarly, urban public green spaces often presented clearer boundaries. However, as mentioned earlier, challenges arise when there is a substantial amount of noise within certain cultivated land or public green space areas, as illustrated in Graph 1 in Figure 9. Furthermore, the segmentation of green belt areas within the city or the green spaces around buildings proved particularly challenging. Notably, the segmentation of urban green areas remains possible in some high-resolution images where arable land is absent, as seen in Graph 8 in Figure 14. However, the segmentation of urban green belts becomes notably challenging in images with a multitude of distributed feature types, as seen in Graph 10 in Figure 8.  
To enhance the experiment's credibility, we conducted re-tests using images of different resolutions. The content information of the images was standardized, and high-resolution images (0.5 m) were resampled/compressed to 1 m, 3 m, 10 m, 20 m, and 30 m resolutions. Four image datasets representing the distribution of ARSIs and UGSs were selected as test data. We aimed to verify the impact of resolution on SAM segmentation accuracy while maintaining uniform information across images and changing only the image resolution. The results are presented in Figure 14, with Figures 15 and 16 providing numerical statistical visualizations of accuracy for the two segmentation methods. Regarding the results of condition-based SAM segmentation, it is evident that certain images exhibit stability in terms of smoothness. However, for certain images like Graph Sets 1 and 3 in Figure 14, SAM segmentation gradually weakens as the resolution decreases. These images display uniform characteristics with scattered noise in cultivated or urban areas, such as the scattered woodland in the middle of the cultivated area in Graph 1 of Figure 9.0 High-resolution images allow for more accurate noise removal by increasing labeled points during training, but for low-resolution images, increasing training points appears to have minimal impact. Additionally, image segmentation accuracy exhibits more obvious differences, with the  
Figure 9.1 Visualization of test results for the same study area size at different resolutions, DATA NO. represents the number of images segmented for each data resolution type. Each data point in the figure is identified by a unique number. best segmentation observed in Graph Set 2 in Figure 14, featuring clear boundaries and stable textures. This further supports the earlier findings that the image type has a more significant impact on SAM segmentation compared to resolution. Additionally, variations in image types also influence the sensitivity of SAM to changes in resolution. Figure 9.2 Image segmentation and comparison diagram of the same location with different resolutions based on SAM. Figure 9.3 Statistical graph of test results of image segmentation with different resolutions at the same location (conditional).