# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 5 Conclusions  
In this paper, we proposed a novel \"Image + Text\" framework for UFZs recognition by integrating GF-7 multi-spectral images, urban 3D information, and POI social perception data. The framework utilizes ST-CNN for extracting image features and LDA for extracting semantic features. ST-CNN benefits from a pre-trained model and requires only a small number of samples, while effectively incorporating the multi-spectral and multi-dimensional features of NIR and nDSM images. The experimental results showed that the proposed framework enhances the accuracy of UFZs identification and accelerates the model training process. The inclusion of NIR, nDSM, and POI data can improve the identification accuracy of UFZs. In the future, we will explore automatic segmentation methods of urban functional units [70], introduce social perception data such as nighttime light images and street view images [39; 71], and explore more effective multimodal data fusion strategies.  
Conceptualization, Z.S.; methodology, P.L. and Z.S.; validation, Y.S. and P.L.; writing--original draft preparation, Z.S. and P.L.; writing--review and editing, D.W., Q.M., and W.Z.; funding acquisition, Z.S. and D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tianjin Municipal Education Commission Scientific Research Program (grant number 2021 SK003); the Tianjin Educational Science Planning Project (grant number EHE210290); the Tianjin outstanding science and Technology Commissioner Project (grant number 22 YDTPJC00230); and the National Natural Science Foundation of China (grant number 41971310). National Natural Science Foundation of China (grant number 42171357); National Key Research and Development Program of China (grant number YFC3301602).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Data Source** & **Study Area** & **Spatial** & **OA** \\ \hline Integrating bottom-up & & & & \\ classification and & & & & \\ top-down feedback [66] & & & & \\ Hierarchical semantic & QuickBird image; & & & \\ cognition [67] & POIs & & & \\ Similarity measures and & Landsat image; & & & \\ threshold [68] & POIs & & & \\ \begin{tabular}{c} Integrating high spatial \\ resolution nighttime light \\ and daytime multi-view \\ imagery based on B-OVW \\ model [69] \\ \end{tabular} & \begin{tabular}{c} Ziyuan3 (ZY3-01) \\ im-age \\ Jilin1-07 (JL1-07) \\ im-age \\ \end{tabular} & \begin{tabular}{c} Beijing, China \\ (300 km2) \\ \end{tabular} & \begin{tabular}{c} Residential, commercial, shantytown, \\ industrial, campuses, park \\ Level I classes: agriculture, green \\ space, waterbody, undeveloped, \\ residential, commercial, \\ industrial, institutional \\ \end{tabular} & 84\% \\ \begin{tabular}{c} Integrating high spatial \\ resolution nighttime light \\ and daytime multi-view \\ imagery based on B-OVW \\ model [69] \\ \end{tabular} & \begin{tabular}{c} 90.8\% \\ \end{tabular} \\ Our method & GF-7 image; POIs & \
\begin{tabular}{c} Beijing, China \\ (300 km2) \\ \end{tabular} &
\begin{tabular}{c} Residential, commercial, shantytown, \\ industrial, campuses, park \\ park, and green space \\ \end{tabular} & 89.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons of the existing methods for UFZs identification.