# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 1 Introduction
However, it is important to note that POI data have limitations due to the uneven distribution and biased categorization of human activities [1]. Fusing remote sensing images and social perception data is an effective way to quickly and accurately identify UFZs. From the perspective of data, most studies on identifying UFZs focus on two-dimensional (2D) data, overlooking the importance of 3D urban morphology information. However, certain urban functional units have differences in urban morphology information, such as office business districts, shopping malls, residential quarters, and shantytowns. Several previous studies have demonstrated that incorporating 3D urban morphology can enhance the accuracy of UFZs identification. The generation of 3D urban information typically relies on building vector data obtained from Internet maps or government departments. However, due to differences in data collection time or quality, these vector data may not be entirely consistent with the actual situation and ignore the 3D information of other ground objects except buildings [33; 34; 35]. Some researchers, such as [PERSON] et al. [36], have utilized the building height information estimated from building shadows to classify urban building functions, improving the recognition accuracy of residential buildings. Nonetheless, this method depends on parameters, such as the sun and satellite altitude, and is not universally applicable due to irregularities in shadow shape area, adhesion, and other phenomena. Airborne Lidar data have been employed to construct 3D urban structure parameters for UFZs classification and achieved good results [37; 38]. However, the collection of airborne Lidar data collection is costly and its coverage is limited. [PERSON] et al. [39] introduced ZY-3 multi-view images into the model to describe the 3D urban morphology information, demonstrating the helpfulness of multi-view images in UFZs identification. Nevertheless, this method does not directly account for the influence of ground objects' height on the UFZs identification. Currently, several high-resolution stereoscopic mapping satellites have been launched, enabling the rapid acquisition of digital surface model (DSM) [40; 41; 42]. GF-7 is one of the most advanced stereo observation satellites in China and generates DSM data with an elevation Root Mean Square Error (RMSE) within 1m, providing large-scale and detailed three-dimensional information about urban ground objects [43]. The fusion of GF-7 multi-spectral images and panchromatic stereoscopic images effectively captures the 2D and 3D features of urban objects, harnessing the advantages of GF-7 multi-view images [44; 45]. In summary, we proposed a simple and effective \"Image + Text\" fusion strategy for UFZs recognition, utilizing GF-7 multi-view and multi-spectral images and POI data. The main contributions are as follows:  
1. We extract image features using a semi-transfer learning strategy and employ the LDA topic model to generate POI semantic features. These features are then fused to improve UFZ recognition. 2. We incorporate both the 2D and 3D characteristics of the study area. 3. We investigate the function of DSM generated from GF-7 images, NIR, and POI in identifying UFZs.