A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method

• Published in: Computers, environment and urban systems environment and urban systems, 2022-07, Vol.95, p.101807, Article 101807
• Main Authors: Xu, Yongyang, Zhou, Bo, Jin, Shuai, Xie, Xuejing, Chen, Zhanlong, Hu, Sheng, He, Nan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Context; Crowdsourcing; Data structures; Experiments; Graph convolution; Graph theory; Land use; Machine learning; Nodes; Points of interest; Resource allocation; Spatial analysis; Spatial context; Spatial distribution; Urban land use; Urban planning; Urban scene
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2022.101807

Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. •POIs are organized by a graph to extract more 2D spatial relationships.•A graph convolutional network is used to learn the spatial relationships, which can improve the performance of urban-use classification.•A case study in the intra-urban area of Beijing, China is constructed.