# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 1 Introduction  
As the basic units of urban, UFZs are the spatial carriers of various social and economic activities, such as commercial zones, residential zones, and industrial zones [1, 2, 3]. With the rapid expansion of cities, a large number of people and means of production gather in the cities, resulting in an increasingly complex urban functional structure. Therefore, the accurate and rapid identification of UFZs holds substantial research and application value. It aids in understanding the interplay between human spatial activities and socioeconomic operations [4]. Moreover, the spatial distribution of urban functions can be utilized to guideurban planning management, resource allocation, environmental monitoring, population estimation, and other related activities [5; 6; 7]. Traditional methods for identifying UFZs have relied on statistical investigation and expert judgment. However, these methods often require field investigation and human judgment, which are highly subjective and time-consuming [8; 9]. In recent decades, with the rapid development of remote sensing technology, remote sensing images have been widely used in urban research fields, including land cover mapping, environmental protection, and deformation monitoring [10; 11; 12]. These studies provide new ideas for the identification of UFZs. Based on different image feature extraction methods, UFZs recognition methods can be broadly divided into two categories. The first category is based on artificially designed features. Some methods employ probabilistic topic mode (PTM) to classify UFZs or scenes by extracting shallow features from images, such as spectral, texture, structure, scale-invariant feature transform (SIFT), and other features [13; 14; 15; 16; 17]. Other methods involve constructing urban landscape features by obtaining urban land cover maps through remote sensing images and then using machine learning techniques to identify UFZs [18; 19; 20]. However, this type of method requires researchers to possess extensive experience and knowledge, to determine the best feature combination through multiple experiments. Moreover, the optimal feature combination often lacks effective transferability between different cities, limiting its generalization. The second category focuses on automatically extracting image features using deep learning methods. With the wide application of deep learning technology in the field of remote sensing, its advantages in identifying UFZs have gradually become prominent [21; 22; 23; 24; 25]. However, most pre-trained models only accept RGB images, while high-resolution remote sensing satellites can acquire RGB + NIR multi-spectral images, and even some satellites provide multi-view and more band information, which limits the direct use of the \"pre-training and fine-tuning\" method for model training on multi-spectral images. To address this challenge, [PERSON] et al. [26] designed a semi-transferred convolutional neural network for UFZs recognition. However, their method relied on the AlexNet pre-training model [27], without considering the impact of other pre-training models on UFZ identification. Additionally, their classification of UFZs was based solely on remote sensing images, so it needs further improvement. Remote sensing images provide information about the natural landscape of cities, but they lack detailed insight into human economic activities in detail. To overcome this limitation, previous studies have used social perception data such as social media data, traffic data, Weibo check-in data, and POI data to identify UFZs. This type of data can effectively reflect the socioeconomic attributes of urban functional units and finely identify different types of UFZs [28; 29; 30; 31; 32]. Among these data sources, POI data are particularly useful as they can be freely obtained from Internet maps without infringing on user privacy. It has been widely used in the identification of UFZs.