Domain shifts induced by variations in urban scenes and adverse weather conditions pose a major challenge for semantic segmentation models in traffic environments. While adversarial domain adaptation has shown effectiveness in reducing cross-domain discrepancies, most existing approaches operate at a single adaptation level and struggle to generalize across diverse degradation patterns. In this work, we propose DA2LF, a dual-level domain adaptation framework designed to improve segmentation robustness under adverse conditions. At the input level, DA2LF reduces low-level appearance gaps via spectral transfer and targeted data augmentation. At the feature level, class-conditional adversarial learning is employed to align semantic representations between source and target domains. To further enhance fine-grained structural consistency, we introduce a multi-resolution Laplacian pyramid that preserves boundary details and small objects. Extensive experiments on both synthetic and real-world benchmarks demonstrate the effectiveness of the proposed approach. DA2LF is evaluated on three standard domain adaptation benchmarks: Cityscapes to Foggy Cityscapes, Cityscapes to Rainy Cityscapes, Cityscapes to ACDC, and consistently outperforms state-of-the-art methods based on global and class-wise alignment.