# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results  
### Comparison Experiment  
#### 3.1.1 Comparison Experiment of the UGS-1 Dataset  
To verify the effectiveness and rationality of MAFANet in UGS segmentation tasks, we select some existing methods and conduct a set of comparative experiments on UGS-1. This experimental method included UNet [18], FCN88 [19], SegNet [20], DeepLabv3+ [21], DenseASPP [31], ShuffleNetV2 [32], PSPNet [33], DFANet [34], DABNet [35], ESPNetv2 [36], ACFNet [37], ERFNet [38], HRNet [39], DensASPP (mobilenet), MFFTNet [40] and MAFANet. Table 1 shows the specific comparison experimental results. Table 1 shows the four score indicators of our selected methods on the UGS-1 dataset. This paper uses PA, MPA, MIOU, and IOU to evaluate the effect of our selected methods. As can be seen from the table, for the UGS segmentation task, the proposed MAFANet has the highest segmentation accuracy, and all indicators are better than other networks. The scores of the four indicators are: PA, 88.52%; MPA, 81.55%; MIOU, 72.15%; IOU (low vegetation), 49.53%; IOU (tree), 81.64% (Table 1). There are objects of different sizes, shapes, and widespread distribution in the HRS images, and the training and predicting results of different DL models are different. In order to intuitively show the effectiveness of the MAFANet model in the UGS segmentation task from the HRS images, this paper selected several representative remote sensing images on the UGS-1 dataset for experiments. The visual segmentation results are shown in Figure 9. We can observe that the MAFANet network can effectively detect the vast majority of objects. It can be seen closely from the first line of Figure 9, although DensASPP uses a dense pyramid and a large field of view structure, the classification results obtained ignore a large number of low vegetation parts, and the classification results are poor. The classification results of DeepLabv3+ show that the edges of the target are relatively smooth, but the building area next to the low vegetation cannot be correctly identified and is mistakenly detected as low vegetation (third row d in Figure 9); at the same time, the low vegetation area next to the trees cannot be effectively identified. UNet classification results have improved, but this method mistakenly identified some barren wastlands as green spaces (Figure 9). We can see from the figure that the segmentation effect of MAFANet is better than that of UNet and DenseASPP, and the edge of MAFANet segmentation is more consistent with the actual edge characteristics of UGS area. The MIOU scores of the MAFANet network are 6.47%, 2.22%, 2.18%, 1.73%, and 1.22% higher than those of ESPNetv2, SegNet, HRNet, ACFNet, and PSPNet, respectively. The IOU (low vegetation) scores of the MAFANet network are 7.78%, 1.94%, 3.13%, 1.69%, and 0.87% higher than those of ESPNetv2, SegNet, HRNet, ACFNet, and PSPNet, respectively. The scores of IOU (tree) are 6.01%, 2.05%, 1.72%, 1.53%, and 1.48% higher than ESPNetv2, SegNet, HRNet, ACFNet, and PSPNet, respectively. Experiments have shown that this model has certain advantages in UGS segmentation from HRS images. At the same time, compared with most existing networks, such as DenseASPP, DensASPP (mobilenet), SegNet, DeepLabv3+, and UNet, MAFANet effectively identifies most UGS and is close to the real surface conditions. Accurate green space segmentation faces considerable challenges, especially in urban environments characterized by intricate surfaces from the HRS images. The presence of building shadows, a multitude of imaging conditions, and the spectral similarities between green spaces and other features significantly impede precise green space extraction. This complexity necessitates innovative approaches to overcome these hurdles and enhance the accuracy of green space segmentation in HRS images. Performance of UGS recognition near buildings by Deeplabv3+ and DenseASPP decline; UNet incorrectly extracts green space parts near buildings, and the overall performance is poor. By optimizing the network, the