SAM4RoadEx: A Segment Anything Model-Based Framework for Road Extraction from Remote Sensing Images with Prompt-Guided Active Supervision

Road extraction from remote sensing images is crucial for autonomous driving, urban planning, and disaster response, yet remains challenging due to the presence of complex interference, the elongated structure of roads, and occlusions by surrounding objects. To tackle these challenges, we propose SAM4RoadEx, a dedicated architecture for road extraction from remote sensing images. First, the encoder incorporates the pre trained knowledge of SAM to effectively model road features and suppress background noise. Second, a Structure-Aware Guided Decoder (SAGD) is specifically designed to capture elongated road structures, enabling accurate and complete road extraction. In particular, it first applies cross-attention between learnable road tokens and image features to capture global structure. Then, it uses self-attention to refine spatial continuity. Finally, a Prompt Guided Active Supervision (PGAS) strategy is designed to provide guidance in severely occluded or discontinuous predictions. During training, PGAS evaluates prediction confidence and, when necessary, dynamically generates point prompts in uncertain regions using distance-transform assessment to guide iterative refinement. These prompts guide the network to focus on discontinuous areas and iteratively refine its predictions. Experiments on the widely used DeepGlobe Road, Massachusetts Road, and CHN6-CUG datasets show that our method outperforms several state-of-the-art methods, achieving an IoU increase of 1.03–4.74% and an APLS increase of 1.22-5.60%. SAM4RoadEx achieves superior performance over several state-of-the-art methods.