# Building Function Recognition Using the Semi-Supervised Classification
###### Abstract  
The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results.  
graph neural network; semi-supervised learning; building function classification; POI 202212990  
generally based on multi-source data in different regions [13]. [PERSON] et al. introduced time utilization activity analysis with the existing domestic building function standards to address this issue [3]. Remote sensing images and the existing urban land use/cover data with a Bayesian network were used to classify buildings [14]. These classification methods are based on deep learning, which extracts feature information (individual and overall spatial feature information) as much as possible from buildings and building groups, as well as other multi-source data for auxiliary analysis. In the era of big data, deep learning plays an important role in scientific research within different disciplines [15].  
Some recent studies showed the advantages of mining information from different data sources and deep learning methods. However, how to the context information from spatial vector data and how to train an intelligent model with less labeled samples are urgent problems for urban building function recognition. This research introduces a new semi-supervised learning model, called the Unified Message Passing model (UniMP), which can be trained by less labeled samples. Moreover, a Graph Transformer jointly labels embedding to propagate both the feature and label information in the UniMP, and it can extract more context information of node label and node features in the graph. In addition, POIs and building footprints are combined to extract the building geometric and attribute information. One-hot encoding was used to describe the attributes of the building by POIs, and eight geometric features of buildings were defined, including building height, circumscribed circle radius, building orientation, minimum enclosing rectangle area, compactness, complexity, density and shape.