A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method. (July 2022)

• Record Type: Journal Article
• Authors: Xu, Yongyang; Zhou, Bo; Jin, Shuai; Xie, Xuejing; Chen, Zhanlong; Hu, Sheng; He, Nan
• Is Part Of: Computers, environment and urban systems. Volume 95(2022)
• Publication Date: 2022-07
• Subjects: Urban land use -- Points of interest -- Urban scene -- Spatial context -- Graph convolution; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101807
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 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. Highlights: 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.