AnyRoad: A Frequency-Aware Adapter Framework for Road Segmentation with Segment Anything Model

Automated road extraction from high-resolution satellite imagery is critical for geospatial applications. However, accurate segmentation requires balancing global topological continuity with local boundary precision. Existing methods often struggle with this trade-off, while directly adapting large-scale foundation models introduces challenges with geometric discontinuities and computational cost. We propose AnyRoad, an asymmetric dual-encoder framework integrating a trainable SegFormer for domain semantics and a frozen SAM-2 for universal structural priors. To fuse these distinct representations, we introduce a Frequency-domain Collaborative Fusion Module (FCFM). Using the Discrete Wavelet Transform (DWT), FCFM decouples features: low-frequency components are aligned via bidirectional cross-attention to preserve macro-level connectivity, while high-frequency details are processed with a WaveMLP-based anisotropic operator to refine geometric boundaries. A Deformable UNet++ decoder is then employed to accommodate diverse road shapes. Experiments on the Massachusetts Roads and DeepGlobe datasets show that AnyRoad performs well. Cross-regional tests on the LSRV dataset also show stable transfer to unseen geographic areas.