CADF-Net: A Conflict-Aware Adaptive Distillation Network for Fusing Multi-Source Land-Cover Products for Key Vegetation Classes in Cross-Border Regions

Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification instability near national boundaries undermines transboundary comparisons. To address this, we propose a Conflict-aware Adaptive Distillation Fusion Network (CADF-Net) that fuses multi-source land-cover products to improve the discrimination and spatial consistency of key vegetation classes in cross-border regions. Taking the transnational China–Russia border (Sanjiang Plain and Primorskiy Kray) as a representative case, we integrate geo-environmental factors and introduce a pixel-level Conflict Index (CI) to explicitly steer the model toward discrepancy-prone areas. Building on this, we develop an Adaptive Distillation U-Net (AD-UNet) with uncertainty-adaptive distillation and employ a confidence-guided, dynamically weighted ensemble to generate the final fused land-cover product (CADF-LC). Quantitative assessments demonstrate that CADF-LC achieved an OA of 0.8600, a Kappa of 0.8133, and an mIoU of 0.7589, outperforming all input land-cover products. Compared with the strongest input product, Esri Land Cover, CADF-LC improved OA by 0.0150 and mIoU by 0.0222. Furthermore, it effectively mitigates the trade-off between detail loss and morphological fragmentation. Ultimately, CADF-Net enhances classification stability for key vegetation classes, offering a reliable foundation for transboundary ecological monitoring and land management.