AFDANet: An Adaptive Full-Stage Feature Fusion and Directional-Aware Network for Road Extraction in Remote Sensing Images

Road extraction from remote sensing imagery is a crucial task in fields such as intelligent transportation. However, challenges remain in restoring road details in complex scenes, multiscale modeling, and ensuring computational efficiency. To address these issues, this study proposes AFDANet, whose core components include: 1) the spatial-enhanced adaptive feature fusion (SEAF) module, which dynamically integrates shallow detail features with deep semantic features. This module incorporates a novel parallel channel-spatial attention (PCSA) mechanism. PCSA effectively decouples channel and spatial information, independently modeling semantic and spatial dimensions, thereby enhancing the model’s ability to distinguish roads from complex backgrounds; and 2) the strip grouped aggregation decoder (SGADecoder), which combines multiscale multidirectional strip convolutions with residual connections, effectively restores road continuity and edge details. Experimental results on the DeepGlobe and Massachusetts datasets demonstrate that AFDANet outperforms existing advanced methods across multiple key metrics while requiring fewer parameters and lower computational costs. This study provides a high-accuracy and computationally efficient solution for road extraction from remote sensing imagery. The source code is publicly available at https://github.com/ZehuaChenLab/AFDANet