Reservoir-enhanced segment anything model for subsurface diagnosis.

Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities. Ground Penetrating Radar effectively visualizes underground conditions using electromagnetic waves; however, accurate anomaly detection through this method remains challenging due to limited labeled data, varying subsurface conditions, and indistinct target boundaries. Although visually image-like, radar cross-sectional data fundamentally represent electromagnetic waves, with variations within and between waves critical for identifying anomalies. Addressing these, we propose the Reservoir-enhanced Segment Anything Model, a framework exploiting both visual discernibility and wave-changing properties of radar data. The model initially identifies visually apparent candidate anomaly regions and further refines them by analyzing anomaly-induced changes within and between electromagnetic waves in local radar scans, enabling precise and complete anomaly region extraction and category determination. Real-world experiments demonstrate that the model achieves high detection accuracy ( > 85%) and outperforms existing methods. Notably, it requires only minimal accessible non-target data, avoids intensive training, and supports both fully automatic operation and simple human interaction to enhance reliability. Our research provides a scalable, resource-efficient solution for rapid subsurface anomaly detection across diverse environments, improving urban safety monitoring while reducing manual effort and computational cost.