Object-Based Urban Land-Use Change Detection With Siamese Network and Hierarchical Clustering

Urban land use (ULU) undergoes continuous and dynamic changes, profoundly reshaping the spatial configuration of urban areas. Accurately quantifying these changes is critical for the precise planning and efficient management of limited urban land resources during urbanization. However, existing change detection methods struggle to address the unique challenges of ULU change detection, leading to a lack of reliable ULU change maps. Two major challenges hinder the study of ULU changes: (1) high internal heterogeneity, which obscures the main features of ULU, and (2) inaccurate boundary delineation. To overcome these challenges, we propose an object-based method for detecting ULU changes. Our approach employs a Siamese neural network equipped with a self-attention mechanism to extract global features, a Fusion module to integrate global and local features, and a multi-scale Amplifier to filter and enhance the main features. Subsequently, a hierarchical clustering framework aggregates the segmented objects with the main features into coherent ULU units. Experimental results demonstrate that the proposed method effectively addresses the two major challenges of ULU change detection. The proposed method achieves the best performance compared to representative methods in Beijing and Shanghai, two of China’s largest and most typical cities. Furthermore, the analysis of ULU change results reveals that both cities experienced rapid outward urban expansion between 2015 and 2020.