## 3 Identification and Division of Urban Functional Zones
### Urban Functional Zone Division Based on Deep Learning  
Deep learning can effectively extract high-level feature information from remote sensing images. [PERSON], et al. [47] used the greedy algorithm and Google Word2 Vec to extract high-dimensional characteristic vectors from the POI data and TAZs to classify urban land use. Some scholars found that it is important to consider the linear relationship between POIs and the nonlinear spatial relationship between a given POI and the surrounding POIs. [PERSON], et al. [98] took into account the spatial relationship between POI data and extended the Word2 Vac model to the Place2 Vac model. [PERSON], et al. [99] used the Place2 Vac model to identify urban functional regions and implemented the model in Tensor-Flow based on the Mini-Batch Gradient Descent. However, this method had a high acquisition cost and was time-consuming. [PERSON], et al. [100] improved the method and achieved low-cost, efficient, and accurate functional zone identification. [PERSON], et al. [101] used the spatial co-location model method to determine the nonlinear spatial relationship of POIs. The comparative experimental results proved that the recognition accuracy of the functional regions was higher for this method than the Place2 Vec method. [PERSON], et al. [102] used the deep semantic segmentation network D-Link Net to extract buildings to calculate the building level metrics based on the Baidu PaddlePaddle open-source framework and Senta sentiment analysis system. Multisource heterogeneous data were integrated to identify the categories of functional zones, and sentiment analysis was conducted to characterize typical functional zones. Convolutional neural networks (CNNs) have been widely used recently for urban functional zone identification due to their strong ability to extract image features and excellent performance in image segmentation and geographic object-based image analysis (GEOBIA). Deep learning methods require massive amounts of training data to determine the network parameters and perform automatic learning of the features. [PERSON], et al. [103] proposed the large-patch CNN (LPCNN) model to address the problem of insufficient markers in remote sensing images ([PERSON] et al., 2015). This model expands the training data of small-scale remote sensing image data set. [PERSON], et al. [104] used CNN and remote sensing images to classify land use. [PERSON], et al. [105] compared three CNN training methods for land use and land cover classification, i.e., full training, fine-tuning, and pre-training, and concluded that fine-tuning of the pre-trained CNN network model provided the best results. [PERSON], et al. [49] used the AlexNet model and Dropout technology to avoid over-fitting and reduce the scale effect and realized fine classification of urban functional zones by Super Object (SO). [PERSON], et al. [51] used the deep-feature convolutional neural network (DFCNN) to mine building physical semantics and social function semantics, which selected TensorFlow1.7 as the deep learning framework on the Ubuntu 16.04 operating system. The proposed model detected urban functional zones more effectively and accurately, compared with Alex Net and random forest. Deep learning is well suitable for extracting high-level information, and its migration and scalability have unlimited potential for urban functional zone identification and analysis. Thus, this method is worthy of further exploration. The identification and division methods of urban functional zones in the above articles are summarized in Table 1.