Investigating Very-High-Resolution Land Cover Mapping in the Pearl River Delta with Remote Sensing Foundation Models and Multi-Source Data Bayesian Fusion

Very-high-resolution (VHR) land cover mapping in highly heterogeneous regions faces critical challenges including strong annotation dependence, significant image heterogeneity, and insufficient spectral information. To address these challenges, this study proposes a novel framework integrating remote sensing foundation models with multi-source data Bayesian fusion for VHR land cover mapping in the Pearl River Delta (PRD), which is one of the most complex and heterogeneous landscapes in China. To implement this framework, we first construct three datasets including PRD262K containing 262,436 unlabeled VHR images for pretraining, PRDLC-PRO with 33,342 annotated samples for semantic segmentation, and a 15,000-point sample set for medium-resolution (MR) classification. A Segmentation-Driven Masked AutoEncoder (SDMAE) is developed to learn robust feature representations from large-scale unlabeled VHR imagery, which is subsequently integrated with a Scene-Based Feature Network (SBFNet) to capture multi-scale semantic features for accurate land cover segmentation. Finally, a decision-level Bayesian fusion method is proposed to effectively integrate the fine spatial details of VHR imagery with the spectral stability of MR data. Experiments demonstrate that the proposed framework outperforms existing methods across multiple datasets, achieving an overall accuracy of 87.98% and mIoU of 66.61% on PRDLC-PRO. The subsequent decision-level Bayesian fusion further enhances spatial consistency and robustness, providing an effective solution for large-scale VHR land cover mapping in highly heterogeneous regions with limited annotations.