## 2 Evolution of AI in GPR Interpretation
### Datasets and Evaluation Practices  
The effectiveness of AI models in GPR interpretation is highly dependent on the availability and quality of training datasets. However, the scarcity of publicly accessible GPR datasets continues to hinder large-scale benchmarking, model comparison, and the reproducibility of results. Despite the growing interest in AI-driven GPR applications, only a limited number of datasets are widely used within the research community. Among them, gprMax stands out as a prominent open-source simulation tool that generates synthetic GPR data using finite-difference time-domain methods, enabling controlled experimenta  
Figure 3: Overview of hybrid pipeline combining physics-based simulation with deep learning. Simulated GPR data generated from known subsurface models is used to train or fine-tune deep learning models. These models then generalize to real GPR data for tasks such as object detection, layer segmentation, or anomaly classification. This approach leverages interpretability of physical modeling and predictive power of AI.  
tion and training data generation. Real-world datasets exported from commercial software such as Radan and ReflexW are also utilized, primarily in academic and industrial studies; however, their availability is often restricted due to licensing issues or proprietary concerns. In addition, many researchers develop custom field datasets tailored to specific application domains, such as utility detection at dedicated test sites or subsurface mapping in archeological investigations. While these datasets offer valuable domain-specific information, they are typically not standardized or openly shared, limiting their broader impact. The lack of centralized repositories or unified formats further complicates dataset accessibility and interoperability across research groups. This fragmented landscape poses significant challenges to the development, evaluation, and generalization of AI models, emphasizing the urgent need for collaborative efforts to build comprehensive, high-quality, and openly available GPR datasets that can drive innovation and facilitate robust comparisons across different AI methodologies. Addressing this issue is essential for advancing the field and bridging the gap between academic research and the practical deployment of AI in GPR interpretation [33; 34].  
To enhance the diversity and volume of training data in GPR interpretation, researchers frequently utilize data augmentation techniques such as flipping, cropping, noise injection, and the synthetic overlay of hyperbolas. These methods help mitigate data scarcity and improve model generalization. Despite these efforts, there remains a critical need for standardized, annotated datasets that comprehensively represent a wide range of soil conditions, subsurface object types, and GPR system configurations. The absence of such benchmarks hampers the fair comparison and reproducibility of AI models in this domain. When evaluating model performance, researchers typically adopt metrics tailored to specific tasks: Accuracy, Precision, Recall, and F1-score are commonly used for classification tasks; Intersection-over-Union (IoU) and Dice coefficient are employed for segmentation tasks; and Mean Average Precision (mAP) is the standard for object detection performance. However, the evaluation landscape lacks consistency and methodological rigor. Many studies do not incorporate essential validation practices such as cross-validation, ablation studies, or domain adaptation testing. This oversight limits our understanding of how well models generalize across different conditions and use cases, and poses challenges when seeking to assess robustness and practical applicability. Without unified evaluation protocols and diverse, high-quality datasets, the progress of AI-driven GPR interpretation risks being fragmented and difficult to benchmark reliably across research efforts [35; 36; 37].