Urban expansion identification and change analysis in Panjin China from 1990 to 2020.

ABSTRACT: This study examines the dynamic mapping of impervious surface changes in optimising urban spatial structures and fostering sustainable development. A novel deep learning model and time-spectral-texture combination optimisation method were employed to identify pixel-based land-cover change trajectories. A piecewise linear regression model was also utilised to determine the time nodes of urban expansion. This methodology was applied to Panjin City, a resource-based city in China, to analyse temporal and spatial morphological changes related to urban expansion. The results reveal that the combination optimisation method achieved a trajectory classification accuracy of 93.10% and macro F1-score of 92.44%, with an urban expansion time identification accuracy of 84.24%. Panjin City's built-up ar

SUBMITTER: Jiang Z 

PROVIDER: S-EPMC12764777 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature