Integrating open geographic data for urban land use classification using graph neural networks from high-resolution remote sensing imagery

Xinyi Gai; Mengmeng Li; Guozhong Chu; Kangkai Lou

doi:10.1117/12.3021259

19 January 2024 Integrating open geographic data for urban land use classification using graph neural networks from high-resolution remote sensing imagery

Xinyi Gai, Mengmeng Li, Guozhong Chu, Kangkai Lou

Author Affiliations +

Proceedings Volume 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023); 129800I (2024) https://doi.org/10.1117/12.3021259
Event: Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 2023, Lianyungang, China

Abstract

Extraction of land use information from very high resolution (VHR) images plays a crucial role in urban planning and management. The study aims to extract urban land use information using VHR images and open geographic data using graph neural networks. We first obtained land cover objects using a semantic segmentation model. The spatial topological relationships between land cover objects were then modeled using graph theory and represented as graph-structured data, in which the attributes of graph nodes were computed based upon points of interest (POI) data and classified land cover map. Last, we used graph neural network to learn high-level structural features for urban land use classification. The proposed method was applied to the core urban area of Fuzhou city, China. Results showed that graph neural networks are effective for urban land use classification from VHR images, and integrating open geographic data further improves the accuracy of urban land use classification to 87% compared to the 84%accuracy obtained by using only VHR images. Our method exhibits high potential for extracting fine-grained urban land use in various urban areas.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyi Gai, Mengmeng Li, Guozhong Chu, and Kangkai Lou "Integrating open geographic data for urban land use classification using graph neural networks from high-resolution remote sensing imagery", Proc. SPIE 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 129800I (19 January 2024); https://doi.org/10.1117/12.3021259

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