Urban Land-Use Classification with Multi-Source Self-Supervised Representation Learning and Correlation Modeling

Urban land-use classification aims to identify the functions of land parcels within cities, which can provide a reference for urban planning and environmental monitoring. Existing urban land-use classification methods generally integrate multi-source data to obtain comprehensive information about land parcels. However, these methods ignore the parcels with incomplete modalities, and the correlations between multisource data have not been explored. To this end, an urban land-use classification framework with multi-source self-supervised representation learning and correlation modeling (LUSC) is proposed. Specifically, the LUSC introduces self-supervised approaches to mine the multi-source representations from high-resolution remote sensing (HRS) images and points-of-interest (POIs), which consider the characteristics of parcels. The multi-source information joint learning module (MIL) is designed to explore the correlations between multi-source data, ensuring consistency of multisource data. The comparative and ablation experiments on the land-use dataset covering 34 Chinese provincial cities demonstrate that the proposed LUSC framework outperforms the advanced baselines.