# Building Function Recognition Using the Semi-Supervised Classification
## 2 Literature Review  
With the development of artificial intelligence, deep learning has been used in many geospatial analysis tasks [15; 16; 17; 18; 19], for it can learn the non-line relations from the training dataset. Under the impetus of multi-source of geospatial big data in the past few years [20; 21], a number of deep learning methods and machine learning methods have been proposed to address the building type classifications problem [22]. In order to recognize the building type (detached building, semi-detached building, terraced building, villa, Wilhelminatisyte building, etc.) from very coarse 3D city model data, support vector machines (SVMs) were introduced [23; 24]. Random forest (RF) was also introduced to classify the building's roof into a flat roof, gabled roof, hipped roof, mixed form roof, pitched/shed roof and pyramid roof [25]. The above machine learning methods perform well in building roof type classification; however, none of them are suitable for building function classification. Over a long period in the past, the functional description was characterized by visual features [26] (e.g., spectral, textural and geometrical features). With the extensive application of convolutional neural networks (CNN) in computer vision, a set of neural networks were used to classify the building function from street images [27; 28; 29; 30]. A fusion model was proposed by [PERSON] et al. [31] to analyze the building function classification from aerial and street view images. Several scholars demonstrated that CNNs could be used to multi-label building function classification through Google Street View. HierarchyNet, a hierarchical network, was developed to classify global urban buildings into main and subcategories [32]. Moreover, in satellite imagery data, recently, the mining of information hidden in emerging geospatial big data (e.g., POIs, street view images data) was combined to classify scenes function by some CNNs [33; 34]. Deep learning methods can achieve building function classification, while the accuracy needs to be improved because the contextual information of the building is not considered. Meanwhile, the buildings along roads are easy to obtain street view images, but not all buildings. Therefore, a new method for building function classification is needed. \"Everything is related to everything else, but near things are more related to each other\" is not only Tobler's First Law but also a significant law of geospatial analysis. With the development of big data, technology promotes urban development and scientific progress and, at the same time, brings opportunities and challenges to the research field of artificial intelligence [15]. Graph Convolution Neural Networks (GCN) [35] provide new insight into land use classification [36] and object detection [37; 38] by mining more context information. GCN has shown excellent performance in many fields, such as computer vision [39], speech recognition [40] and natural language processing [41]. Moreover, it performs well on vector data [42] because it takes more neighbor information into account [2; 43]. A building function is largely related to its neighbors; for instance, residential buildings are always built together and away from factorial buildings. Therefore, the GCN is suitable for identifying individual building functions. However, large amounts of labeled data are required to train the model by general GCNs, which is a great challenge for researchers to label a large number of buildings manually. Furthermore, at the stage of model training for node classification by general GCNs, some neighbors and topological relation information are not taken full advantage of [44; 45; 46]. Additionally, it is difficult to classify the building function in a city merely by applying a single data source [47; 48].