Fusion of UAV-Based Structure-from-Motion and Ground Penetrating Radar for Integrated Surface-Subsurface Modeling

The deployment of cameras and ground penetrating radar (GPR) on unmanned aerial vehicles (UAVs) enables the joint capture of high-resolution surface structure and subsurface features. Structure-from-Motion (SfM) photogrammetry generates dense 3D point clouds and surface models, while GPR provides depth-resolved information on buried features. When integrated, these complementary datasets may offer a powerful means to understand spatial relationships across surface and subsurface domains. However, SfM and GPR data can differ significantly in spatial density, resolution, and modality, posing challenges when being combined to support 3D modeling and interpretation. This work explores a sensor fusion framework that combines SfM-derived surface models and UAV-based GPR data to create unified 3D surface-subsurface representations. The approach can be used for cross-domain feature extraction, allowing surface features (e.g., stones placed over a historical site) to help inform subsurface interpretation. The proposed workflow addresses co-registration and alignment of SfM point clouds and GPR volumes, supporting AI detection and classification of features of interest (e.g., buried structures, voids, or boundaries). Applications include 3D representation that can be used to prepare systems to learn joint patterns across the two domains.