# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 3 Results  
### Accuracy and Spatial Distribution of 10 m Parcels  
This study used FCN8s [35], DeepLabv3+ [36], HRNet [37], and UNet++ [38] as baseline models and constructed a 10 m sample set (containing 1560 images) to train these models. The experimental platform is equipped with an RTX 3090 GPU and a 13 th Gen Intel(R) Core(TM) i9-13900K CPU, operating on Windows 11. The software stack includes Pytorch 2.4.1, Python 3.9, and CUDA 12.1. Each model was trained for 200 epochs. Table 2 shows the accuracy comparison between different models. From the experimental areas, all models exhibited high prediction accuracy in Youyi County. In the A1 region, except for the poor performance of UNet++, the prediction accuracy of the models was impressive. In these two regions, although our method could not match the highest-precision method, its overall performance across various accuracy metrics was not inferior to any single model. In the A2 study area, considering various accuracy metrics, our method demonstrated more robust performance than all the baseline models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). In addition, we found that regardless of which model was applied to the A2 study area, the prediction accuracy decreased, proving that accurately identifying parcels in the A2 region indeed presents certain difficulties. Previous studies have also confirmed that the accuracy of parcel extraction in plain areas is higher than in hilly regions [39; 40]. Figure 6 shows a comparison of the extraction results of various models across the three study areas, further validating this point. An analysis of this phenomenon reveals several contributing factors: on one hand, farmland boundaries in plain areas are often demarcated by distinct features such as roads or ditches, which facilitate straightforward parcel extraction. In contrast, fragmented farmland regions are frequently surrounded by forests or grasslands, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{4}{c}{**Youyi**} & \multicolumn{4}{c}{**A1**} & \multicolumn{4}{c}{**A2**} \\ \hline \hline  & **P** & **R** & **F1** & **IoU** & **P** & **R** & **F1** & **IoU** & **P** & **R** & **F1** & **IoU** \\ \hline FCN8s & 0.95 & 0.88 & 0.93 & 0.87 & 0.94 & 0.9 & 0.93 & 0.87 & 0.87 & 0.72 & 0.79 & 0.65 \\ DeepLabv3+ & 0.94 & 0.86 & 0.9 & 0.82 & 0.9 & 0.97 & 0.93 & 0.87 & 0.87 & 0.61 & 0.71 & 0.56 \\ HRNet & 0.94 & 0.88 & 0.92 & 0.84 & 0.9 & 0.97 & 0.93 & 0.88 & 0.93 & 0.62 & 0.74 & 0.59 \\ UNet++ & 0.94 & 0.91 & 0.93 & 0.87 & 0.9 & 0.79 & 0.87 & 0.77 & 0.91 & 0.64 & 0.75 & 0.6 \\ ours & 0.89 & 0.91 & 0.91 & 0.87 & 0.91 & 0.91 & 0.92 & 0.88 & 0.88 & 0.76 & 0.81 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accuracy metrics between the method of this study and other models across the three study areas. which may share similar spectral characteristics with farmland, leading to errors. On the other hand, 10 m imagery suffices for extracting large, regular parcels in plains, where parcel sizes and shapes are relatively uniform. However, in hilly regions, significant variability in parcel size and shape, combined with satellite imagery resolution limitations, constrains models' extraction accuracy. From Figure 6, it can also be observed that compared to DeepLabv3+ and HRNet, FCN8s and UNet++ provide more accurate boundary predictions with less adhesion between parcels. Meanwhile, since SAM focuses on extracting target parcels, the different parcels are independent of each other, which saves time in post-processing. Figure 7 displays the outcomes of parcel extraction using Sentinel imagery across three study areas. The results demonstrate that a significant number of cropland parcels have  
Figure 6: Comparison of parcel extraction results between the method of this study and other models across the three study areas. (**a**) Youyi county. (**b**) A1. (**c**) A2. been accurately extracted, showcasing the robust generalization capabilities of computer vision models like SAM on remote sensing images.