Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images

Urban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. Our method constructs a graph to represent spatial relations between land cover objects as graph nodes within a street block. These nodes are characterized by spatial composition and structure features of their surrounding neighborhood. We then apply unsupervised graph learning to partition the graph into subgraphs, which represent communities spatially bounded by street boundaries and correspond to land use units. Next, a graph neural network is used to extract deep structural features for land use classification. Experiments were conducted using high-resolution satellite images from the cities of Fuzhou and Quanzhou, China. Results showed that our method surpassed traditional grid and street block techniques, improving land use classification accuracy by 24% and 9%, respectively. Furthermore, it achieved classification results comparable to those using reference land use units, with an overall accuracy of 0.87 versus 0.89.