Ground Penetrating Radar for Subsurface Utility Detection: Methods, Challenges, and Future Directions

Ground-penetrating radar (GPR) has applications across many domains, including archaeology, mining, and infrastructure inspection. This review is specifically focused on urban subsurface utility mapping, where accurate detection of buried pipelines, cables, and conduits is critical for excavation safety and infrastructure management. Within this scope, two major barriers are identified: event–utility mismatch and the synthetic–field domain gap. Bibliometric analysis shows increasing reliance on deep learning, yet most methods remain limited to event-level hyperbola detection rather than utility-level inference. In real urban environments, radar responses are often affected by orientation-dependent signatures, clutter, overlapping reflections, and non-utility anomalies, making detected events difficult to map directly to physical infrastructure. In parallel, models trained on synthetic data frequently show limited field generalization because simulated radargrams do not fully reproduce soil heterogeneity, acquisition variability, and system artifacts. The review argues that future progress in urban utility mapping requires a shift toward utility-level reasoning supported by multi-sensor fusion, physics-guided learning, hybrid simulation–field datasets, and uncertainty-aware interpretation. Such advances are essential for making GPR outputs more reliable and actionable in urban engineering practice.