# Evaluating the Efficacy of Segment Anything Model for
## 5 Discussion: Limitations and Future Work  
As evident from our results, SAM demonstrates a distinct advantage in ARSI and UGS image processing and segmentation. Nevertheless, it is undeniable that both resolution and image size have some influence on the segmentation effectiveness of SAM. However, a consistent imaging pattern was not demonstrated, as there was some variation in the pattern and extent among different images across various image types. In terms of resolution, conditional-based segmentation has minimal impact, while the unsupervised segmentation-based approach is susceptible to a certain extent. In some images, particularly those with resolutions reaching 10 m and above, the segmentation accuracy significantly decreases. It is worth noting that the accuracy of segmentation is also influenced by the type of segmented image. Regarding size, as the image size continues to increase, the segmentation accuracy is somewhat affected, especially for images with unclear boundaries and excessive information coverage. This impact becomes more pronounced, emphasizing the importance of image characteristics in determining segmentation accuracy. It is worth noting that SAM prioritizes object-oriented segmentation during the segmentation process, followed by texture and color analysis at the individual object level before merging them seamlessly. 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. The strengths of SAM in the realm of agricultural and UGS remote sensing image segmentation can be summarized as follows:  
1. Preliminary object-oriented segmentation excels in accurately segmenting individual agricultural regions, producing smooth images with minimal noise compared to pixel-based algorithms, as demonstrated in Figure 25's pixel-based segmentation image results;  
Figure 24: Visualization of test results for different study area sizes at the same resolution (conditional). DATA NO. represents the number of images used for each data resolution type. Each data point in the figure is identified by a unique number. The bidirectional arrows at the bottom of the figure signify that those points within the arrow range, irrespective of their shape, correspond to the associated resolution. SAM's processing speed is exceptional with the existing framework of trained models, allowing for zero-shot generalization to unfamiliar objects and images without the need for additional training. SAM in the field of agriculture and UGS remote sensing image segmentation also exhibits some limitations, as outlined below:  
1. For ordinary cultivated land, in terms of conditional segmentation, a small amount of noise will affect the segmentation effect of SAM, and the effect may be less for small size or high resolution. Details are shown in Figures 26 and 27;
2. For green belts inside the city, the segmentation effect based on SAM performs poorly regardless of the resolution or size, and when buildings and green belts are interspersed with each other in the urban system, which is relatively complex to display on the image, as shown in Figure 28, the SAM performance is very weak;
3. Various types of large-sized images, SAM shows poor results, which may be affected by too many types of objects;
4. For unsupervised classification, the segmentation of the images is consistently poor, and the overall accuracy and stability are ineffective. This segmentation method is particularly advantageous for ARSI and UGS images with well-defined edges. However, the limitations of the SAM model become apparent in images with larger sizes or blurred edges, as they are poorly segmented.