###### Abstract  
Artificial intelligence (AI) has emerged as a powerful tool for advancing the interpretation of ground penetrating radar (GPR) data, offering solutions to long-standing challenges in manual analysis, such as subjectivity, inefficiency, and limited scalability. This review investigates recent developments in AI-driven techniques for GPR interpretation, with a focus on machine learning, deep learning, and hybrid approaches that incorporate physical modeling or multimodal data fusion. We systematically analyze the application of these techniques across various domains, including utility detection, infrastructure monitoring, archeology, and environmental studies. Key findings highlight the success of convolutional neural networks in hyperbola detection, the use of segmentation models for stratigraphic analysis, and the integration of AI with robotic and real-time systems. However, challenges remain with generalization, data scarcity, model interpretability, and operational deployment. We identify promising directions, such as domain adaptation, explainable AI, and edge-compatible solutions for practical implementation. By synthesizing current progress and limitations, this review aims to bridge the gap between theoretical advancements in AI and the practical needs of GPR practitioners, guiding future research towards more reliable, transparent, and field-ready systems.  
ground penetrating radar (GPR); artificial intelligence (AI); deep learning; machine learning; subsurface imaging 2020  
1  
[PERSON], [PERSON], [PERSON], [PERSON], A.M. Bridging Theory and Practice: A Review of AI-Driven Techniques for Ground Penetrating Radar Interpretation.  
but often fall short in reliably extracting high-level information, especially in complex or noisy environments [5, 6, 7, 8, 9].  
Over the past decade, artificial intelligence (AI) has emerged as a transformative solution to many of these limitations. Classic machine learning (ML) techniques, including support vector machines (SVMs), random forests, and clustering algorithms, have demonstrated promise in automating tasks such as object classification and anomaly detection in GPR data. More recently, deep learning (DL) models--especially convolutional neural networks (CNNs) and U-Net-based architectures--have pushed the frontier by achieving superior performance in feature extraction, segmentation, and 3D data analysis. Additionally, hybrid models that incorporate physical knowledge, or fuse GPR with other sensing modalities such as LiDAR and InSAR, show growing potential to overcome generalization and data scarcity issues [10, 11, 12, 13].  
The goal of this review is to provide a comprehensive and structured overview of AI-driven techniques for interpreting GPR data. By bridging the theoretical underpinnings of AI models with practical deployment challenges, we aim to assess how well these methods translate to real-world scenarios. In particular, we focus on the evolution of AI models tailored for GPR, the methodological diversity within this space, and the spectrum of applications that demonstrate their impact [14, 15, 16, 17].  
This review is organized as follows: Section 2 outlines the evolution of AI in GPR interpretation, presenting a historical and conceptual framework for understanding key methodological advances. Section 3 provides a detailed survey of specific techniques, categorized by their underlying AI approach and application domain. Section 4 discusses the translation of theory into practice, examining real-world case studies, performance trade-offs, and interpretability challenges. Finally, Section 5 identifies current gaps, unresolved challenges, and future research directions at the intersection of AI and GPR.