# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Discussion
#### 4.2.3 Contribution of Different Modality Data  
We propose a multimodal data fusion framework for UFZs identification. To analyze the impact of RGB, NIR, nDSM, and POI data on identifying UFZs, we designed five different inputs. The network structure is ST-CNN, and the classifier is SVM. Table 6 presents the recognition results of UFZs with different inputs.  
Table 6 shows the effectiveness of incorporating multimodal data in improving the recognition accuracy of UFZs. When comparing the RGB to the additional NIR data, there is an increase of 0.79% in OA and 0.88% in kappa coefficient. The recognition accuracy of residential zones, commercial zones, development, and education has improved, with enhancements ranging from 0.84% to 5.89%. The inclusion of the nDSM data leads to 0.99% in OA and 1.09% in kappa coefficient. The recognition accuracy of residential zones, commercial zones, education, and shantytown increased, ranging from 0.98% to 11.77%. When adding NIR + nDSM data, the OA and kappa coefficient increased by 1.99% and 2.47%, respectively. The recognition accuracy increases for residential zones, commercial zones, development, education, and shantytown ranging from 1.26% to 11.77%. Compared with the input of RGB + NIR + nDSM, when POIs are added, the OA and kappa coefficient have been greatly improved, increasing by 4.17% and 5.50%, respectively. With the exception of shantytowns, the recognition accuracy for other UFZ categories experiences improvements ranging from 0.99% to 17.65%.  
After adding NIR and nDSM data, the OA and kappa coefficient of UFZs improve. However, the recognition accuracy varies across different categories of UFZs, which may  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **RGB** & **RGB + N** & **RGB + D** & **RGB + N + D** & **RGB + N + D + P** \\ \hline Residential zones & 86.81 & 89.69 & 89.93 & 90.41 & 94.00 \\ Commercial zones & 78.66 & 79.50 & 80.75 & 79.92 & 83.68 \\ Shantytown & 90.20 & 88.24 & 91.18 & 92.16 & 92.16 \\ Public service & 40.54 & 31.08 & 29.73 & 37.84 & 55.41 \\ Development & 91.07 & 94.64 & 91.07 & 94.64 & 96.43 \\ Education & 35.29 & 41.18 & 47.06 & 47.06 & 64.71 \\ Green land & 95.05 & 95.05 & 92.08 & 93.07 & 94.06 \\ OA & 82.01 & 82.80 & 83.00 & 84.00 & 88.17 \\ Kappa coefficient & 75.94 & 76.82 & 77.03 & 78.41 & 83.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of UFZs recognition results with different inputs (N: NIR, D: nDSM, P: POIs).  
Figure 11: Training curves of different models. M1: pre-trained VGG16 (RGB); M2: self-built network (RGB + NIR + nDSM); M3: ST-CNN (RGB); M4: ST-CNN (RGB + NIR + nDSM).  
be attributed to the different urban landscapes associated with each category. For instance, nDSM data enhance the recognition accuracy of residential zones, commercial zones, education, and shantytown with dense buildings. POI data showcase high-level semantic information tightly related to human economic activities, which can improve the recognition accuracy of UFZs, especially for public services and education. Therefore, social perception data such as POIs are important for UFZs identification. However, it should be noted that even with the inclusion of nDSM and POIs, the accuracy of identifying public services and education remains relatively low. For public services, the diversity of POI types associated with public services reduces the effectiveness of the extracted POI semantic characteristics. Additionally, some buildings within the public service are similar to commercial buildings in the image, resulting in similar image features. For education, the low accuracy can be attributed to the small number of original training samples, which may limit the model's generalization ability. Furthermore, educational buildings often exhibit similarities with residential buildings, such as dormitory buildings.