# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results
### Comparison Experiment
#### 3.1.2 Comparison Experiment of the UGS-2 Dataset  
To further test the generalization performance of MAFANet in the UGS segmentation task, we select the existing DL methods to conduct a set of comparative experiments on UGS-2. Table 2 shows the specific comparison experimental results.  
In the UGS-2 dataset, the four score indicators of the proposed MAFANet network are: PA, 90.19%; MPA, 83.10%; MIOU, 74.64%; IOU (low vegetation), 51.43%; IOU (tree), 83.72% (Table 2). Comparing the segmentation accuracy of the MAFANet on UGS-1, the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Overall Results**} & \multicolumn{3}{c}{**Class IOU**} \\ \hline
**Method** & **PA (\%)** & **MPA (\%)** & **MIOU (\%)** & **Low Vegetation (\%)** & **Tree (\%)** \\ \hline DensASPP (mobilenet) & 84.73 & 75.48 & 64.89 & 38.62 & 73.85 \\ DFANet & 87.30 & 77.48 & 68.37 & 41.05 & 78.29 \\ ESPNetv2 & 88.07 & 77.67 & 69.43 & 42.40 & 80.08 \\ DensASPP & 88.45 & 78.73 & 70.58 & 45.00 & 80.80 \\ DeepLabv3+ & 88.53 & 79.63 & 70.88 & 44.94 & 81.06 \\ ShuffleNetV2 & 88.26 & 80.53 & 71.43 & 48.45 & 80.43 \\ FCN8s & 88.87 & 79.87 & 71.58 & 46.57 & 81.27 \\ HRNet & 89.21 & 80.41 & 72.24 & 47.45 & 81.92 \\ ERFNet & 89.42 & 80.81 & 72.71 & 48.25 & 82.11 \\ DABNet & 89.34 & 81.18 & 72.97 & 49.71 & 81.73 \\ ACFNet & 89.47 & 81.81 & 73.35 & 50.29 & 82.06 \\ PSPNet & 89.61 & 81.48 & 73.37 & 49.87 & 82.61 \\ SegNet & 89.59 & 81.49 & 73.40 & 50.17 & 82.49 \\ ResNet50 & 89.84 & 81.70 & 73.82 & 50.71 & 82.68 \\ MFFTNet & 89.82 & 82.27 & 74.01 & 51.24 & 82.63 \\ UNet & 89.97 & 82.29 & 74.09 & 50.71 & 83.28 \\ MAFANet & 90.19 & 83.10 & 74.64 & 51.43 & 83.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of comparison experiments on UGS-2.  
Figure 9: Visualization results of experiments comparing the UGS-1. (**a**) Input image; (**b**) Label; (**c**) DensASPP; (**d**) DeepLabv3+; (**e**) UNet; (**f**) MAFANet.  
MIOU on UGS-2 data set reached 74.64%, an increase of 2.49%. Experiments have shown that using standard false color images to extract urban green spaces can better express the characteristics of UGS. It can be observed that the MAFANet network can effectively detect the vast majority of objects. From the third row of Figure 10, both DensASPP and DeepLabv3+ use dense connection structures, but the classification results obtained ignore a large number of low vegetation parts, and the classification results are poor; in comparison, MAFANet and UNet can effectively identify low vegetation and tree areas, The segmentation edges of MAFANet network are smoother than UNet. DensASPP mistakenly detects some roads between green spaces as green spaces (second row in Figure 10). We can see that MAFANet outperforms DeepLabv3+, DensASPP, and UNet in classifying low vegetation and tree. The MIOU scores of the MAFANet network are 5.21%, 2.4%, 1.29%, 1.27%, and 1.24% higher than those of ESPNetv2, HRNet, ACFNet, PSPNet, and SegNet, respectively. The IOU (low vegetation) scores the MAFANet network are 9.03%, 3.98%, 1.14%, 1.56%, and 1.26% higher than those of ESPNetv2, HRNet, ACFNet, PSPNet, and SegNet, respectively. The scores of IOU (tree) are 3.64%, 1.8%, 1.66%, 1.11%, and 1.23% higher than ESPNetv2, HRNet, ACFNet, PSPNet, and SegNet, respectively. For the surface information of the first row of building attachments, MAFANet recognizes better, captures more complete information, and excels closely approximating real surface conditions.