# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 4 Discussion
### Uncertainty Analysis  
SAM has strong generalization ability, gained from training on a large segmentation dataset, allowing it to segment images not seen during training. However, in remote sensing, challenges arise due to the diversity of sensors, complexity of imaging scenes, randomness of ground object distribution, and irregularity of target shapes. For instance, SAM effectively segments regular features like buildings and roads in remote sensing images but struggles with small or less distinct objects, leading to reduced performance [28]. In addition, remote sensing images contain a large amount of geographical knowledge  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Predict Time (s)** & **Sample Sets** & **Pre-Training** \\ \hline FCN8s & 0.0659 & \(\surd\) & \(\surd\) \\ DeepLabv3+ & 0.1902 & \(\surd\) & \(\surd\) \\ HRNet & 0.0868 & \(\surd\) & \(\surd\) \\ UNet++ & 0.1263 & \(\surd\) & \(\surd\) \\ Ours & 1.3645 & \(\times\) & \(\times\) \\ \hline \hline \end{tabular} Note: Prediction time was obtained by averaging predictions of 100 images (1024 \(\times\) 1024 pixels); \(\surd\) indicates requirements while \(\times\) indicates no need; the SAM Point parameter in our method is 30.  
\end{table}
Table 4: Comparison of prediction time and training data requirements for different models.  
and spatial relationships. As a segmentation model, SAM usually cannot understand this knowledge and these relationships well, resulting in false segmentation. To address this, some a priori knowledge can be applied. In this study, a cultivated land mask was used to remove some non-cropland effects when performing 10 m-resolution parcel extraction. Moreover, due to the complexity of high-resolution images, false segmentation often occurs when using SAM to extract parcels. As shown in Figure 11a-d, segmentation errors are observed under various scenarios: Figure 11a demonstrates misclassification caused by internal structures such as electric towers and trees within parcels; Figure 11b reveals false positives induced by uneven gray-scale distribution; while Figure 11c,d exhibit insufficient segmentation in more complex regions. This collectively proves that SAM's performance is significantly affected by parcel-internal textures, gray-scale variations, and environmental complexities, which not only reduces segmentation accuracy but also increases post-processing challenges. Overall, as a basic model, SAM has shown strong application potential in remote sensing, but in specific research areas, SAM still faces many challenges. Therefore, future research should focus on how to use some constraints or adapt SAM to improve its performance in specialized fields [41; 42].  
In addition to the uncertainties present in SAM, the ESRI land cover data used in this study also contain potential errors. For instance, some non-agricultural areas may be misclassified as cropland, and there is incomplete extraction of cropland in certain regions. Furthermore, due to resolution limitations, a significant number of mixed pixels exist in complex areas and along cropland edges, which to some extent affects the accuracy of this study. Beyond that, this study uses 10 m parcels to crop 0.65 m imagery, introducing a degree of error, particularly in edge areas where some cropland pixels may be lost. One potential solution could be to add a buffer zone to mitigate this issue. However, the size of the buffer zone and its subsequent impact on the calculation of histogram features and edge characteristics remain topics worthy of further discussion.