Accurate land cover mapping at very high spatial resolution is essential for monitoring urban and environmental dynamics, yet the transferability of deep learning models across geographic domains remains a challenge. This study evaluates the transferability and generalization capability of the FLAIR-HUB semantic segmentation model, originally trained on French aerial imagery, when applied to the metropolitan area of Iaşi, Romania. The model is tested across multiple spatial resolutions (0.084 m, 0.2 m, and 0.5 m) and temporal scenarios (2024 vs. 2019), enabling a comprehensive assessment of cross-resolution and temporal robustness. A novel validation framework is introduced, combining conventional 2D raster-based evaluation with a 3D point-wise assessment using semantically labeled UAV-derived point clouds. The results demonstrate strong performance for dominant classes such as buildings and herbaceous vegetation, with improved accuracy at higher spatial resolution, while stable classes such as buildings and impervious surfaces show a comparatively robust performance, confirming the model’s capability to consistently represent invariant land cover types. However, performance decreases for heterogeneous and vegetation-related classes due to seasonal variability and class complexity. The 3D validation reveals slightly lower but consistent results, highlighting its role as a more rigorous evaluation approach. Overall, the study confirms the potential of transferring pre-trained semantic segmentation models to new geographic contexts, while emphasizing the importance of spatial resolution, temporal consistency, and validation strategy.