## 4 Multimodal Fusion for Infrastructure Inspection
### Challenges and Best Practices  
Despite the significant promise of AI techniques in GPR interpretation, their real-world deployment remains fraught with challenges. A primary concern is generalization: models trained under specific soil or environmental conditions often struggle when applied to different sites without further adaptation. Additionally, the scarcity of annotated GPR datasets (due to the high cost and expertise required for manual labeling) limits the performance and reliability of AI models. Interpretability also poses a serious issue, especially in high-stakes applications such as infrastructure monitoring or heritage conservation, where decision-making transparency is crucial; many AI systems still function as opaque  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Platform** & **Sensors Used** & **AI Model** & **Task** & **Deployment Scenario** \\ \hline UAV & GPR + GNSS & Lightweight CNN & Subsurface mapping & Minefields, agriculture \\ Ground Rover & GPR + LiDAR & U-Net & Pipe and rebar & Construction, inspection \\ Handheld Scanner & GPR & ResNet classifier & Target detection & Archaeological survey \\ \hline \hline \end{tabular}
\end{table}
Table 5: AI-GPR applications in field robotics.  
black boxes. Moreover, computational constraints hinder the practical use of advanced models, as field-deployable hardware may not support the resource-intensive requirements of cutting-edge deep learning techniques. To address these limitations, researchers are developing various mitigation strategies. These include data augmentation and the use of synthetic data generation tools such as gprMax, which can create diverse training scenarios to improve model robustness. Transfer learning and domain adaptation are also increasingly adopted to help models generalize better across varied conditions with limited new data. To tackle the interpretability issue, explainable AI (XAI) methods such as Grad-CAM are being integrated to highlight important features contributing to model decisions, enhancing user trust. Furthermore, modular pipeline designs--where detection, classification, and post-processing are separated--are encouraged to improve adaptability, interpretability, and computational efficiency. These strategies are particularly relevant in sensitive contexts such as buried structure mapping in heritage sites, where accurate and non-invasive subsurface analysis is vital. In such applications, combining AI-driven tools with expert domain knowledge and physically informed constraints offers a promising pathway forward for reliable and ethical deployment.  
AI techniques have transformed how GPR data is interpreted, moving from hand-crafted heuristics to robust, adaptive models capable of real-time decision-making. Classic ML methods remain relevant for lightweight tasks, while DL architectures dominate in precision-demanding applications. Hybrid approaches and multimodal learning offer a pathway towards more reliable and interpretable models [71, 72, 73].  
The best practice pipeline for the development of an AI-GPR model is presented at Figure 6. Crucially, the success of these techniques depends not just on technical sophistication but also on thoughtful integration into domain-specific workflows. The following sections of this paper explore how these methods perform in real-world scenarios and the challenges that remain in bridging the gap between theory and practice [74, 75].