A graph-based framework to integrate semantic object/land-use relationships for urban land-use mapping with case studies of Chinese cities

Abstract

Urban land-use types, such as residential and administration, can be inferred through semantic objects and their relationships. Point of interest (POI) data can serve as the semantic objects for urban land-use mapping. However, the previous POI-based approaches have rarely considered the relationships between the semantic objects in the urban land-use mapping, and three main challenges remain: 1) the lack of paired semantic object/land-use samples; 2) the lack of a unified model for semantic objects and the relationships between sematic objects and urban land use; and 3) the difficulty of automatically learning semantic object/land-use mapping relationships. In this paper, to address these issues, a graph-based urban land-use mapping framework integrating semantic object/land-use relationships (GOLR) is proposed. Based on open-source area of interest (AOI) and POI data, an urban object/land-use (UOLU) dataset covering 34 cities in China was built. To model the spatial and mapping relationships, the semantic objects and their relationships are used to jointly build an urban land-use graph. The mapping from semantic objects to urban land use can then be learned by the urban land-use graph isomorphic network (ULGIN) model. Finally, the GOLR framework was applied to obtain accurate land-use mapping results for multiple Chinese cities.

Keywords:

• Urban land-use mapping
• graph convolutional network
• point of interest
• area of interest

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and the codes used in this study are available from https://doi.org/10.6084/m9.figshare.20310489.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant Nos. 42071350 and 42211530032, and LIESMARS Special Research Funding.

Notes on contributors

Yu Su

Yu Su is a student at Wuhan University. Her research interests include urban land-use mapping based on multi-source geographic data. She contributed to the conceptualization, methodology, validation, formal analysis, investigation, data curation, and writing.

Yanfei Zhong

Yanfei Zhong is a professor at Wuhan University. His research interests include remote sensing image interpretation and GIScience. He contributed to the conceptualization, methodology, formal analysis, investigation, resources, writing, supervision, and funding acquisition.

Yinhe Liu

Yinhe Liu is a student at Wuhan University. His research interests include high-resolution remote sensing classification and land-cover mapping. He contributed to the methodology and data curation.

Zhendong Zheng

Zhendong Zheng is a student at Wuhan University. His research interests include remote sensing image scene classification. He contributed to the software and data curation.