# Evaluating the Efficacy of Segment Anything Model for
## 5 Discussion: Limitations and Future Work
In such cases, pixel-based semantic segmentation, such as maximum likelihood classification, appears to be a more suitable choice, as shown in Figure 25, which compares the results of both segmentation methods. For some images that are more complex and heterogeneous, having more types of features, the SAM model performs suboptimally. In general, pixel  
Figure 25: Comparison of SAM-based and pixel-based segmentation results. We used Maximum Likelihood Segmentation, a pixel-based segmentation method, to compare the segmentation results of SAM, and the results show that pixel-based segmentation yields more cluttered and less smooth results, but the pixel-based segmentation approach performs significantly finer and more approximate than SAM on certain images. Meanwhile, SAM, which uses unsupervised segmentation, does not seem to play any role in processing some of the large-size photos, as shown in the upper-right graph, where SAM only segments a small region in the lower-right. based image segmentation offers certain advantages, especially for large-scale images. Upon closer examination of these subpar images, it becomes evident that they lack well-defined boundaries, and the distinctions in color and texture among various features are insufficiently pronounced. Consequently, additional computations are necessary to accentuate these differences. This could indeed be one of the limitations of SAM when it comes to processing agricultural remote sensing images. Overall, the segmentation performance for cultivated land and urban public green spaces is consistently good across various image types, with few exceptions, such as instances of excessive noise or closely matched colors between different ground objects. However, the segmentation of urban green belts, characterized by a more chaotic distribution, tends to be generally poor. Figure 26: Comparison of segmentation performance of noisy plowed fields on different resolution images. Graph 1 illustrates the segmentation results at a resolution of 0.5, and Graph 2 depicts the results at a resolution of 10 m. As indicated by the arrows on the graph, there is a small amount of woodland within the cropland area. These woodlands can be accurately segmented using a small number of sample points on the high-resolution image. However, for the low-resolution image, even with an ample number of sample points added, the result is not satisfactory. The red arrow points to the region we need to segment out. Figure 27: Comparison of segmentation performance of noisy plowed fields on different image sizes. Graph 1 shows the segmentation results for small-size images, and Graph 2 shows the segmentation results for medium-size images. As the arrows in the figure point to the part of the woodland that needs to be eliminated, it is clear to see that the smaller-sized images are segmented better. The red arrow points to the region we need to segment out. Figure 28: Schematic diagram of green space segmentation within the city. In general, when employing conditional green space extraction, it proves challenging to effectively extract green spaces in the presence of small buildings. Conversely, with unsupervised classification, the extraction of buildings is more successful. The red arrow points to the region we need to segment out. Currently, the adoption of SAM in the field of agricultural remote sensing is not widespread. The primary reason for this may be the SAM model's current inability to perform minimal interactive data processing and analysis. Specifically, agricultural researchers or operators, even after segmenting acquired images, are unable to extract useful information from the segmented images and must rely on other data analysis software for further operations. Moreover, the data processing results obtained using SAM lack essential geographic information, which can lead to significant errors in researchers' area or scale measurements. Effective data statistics and visualization are also unfeasible.