The International Association for Automation and Robotics in Construction
Publications / 2024 Proceedings of the 41st ISARC, Lille, France
Exploring Self-Supervised GPR Representation Learning for Building Rooftop Diagnostics
Ground Penetrating Radar (GPR), known for its applications in diverse domains, demonstrates potential for non-destructive diagnostic assessments on building rooftops. This study delves into the unique characteristics and data structure of GPR, investigating the novel approach of processing GPR as a "contextual neighborhood" of A-scans within their respective B-scans as opposed to the typical pixel-based approach. Given the challenge of obtaining a large corpus of annotated rooftop GPR data, we employ self-supervised deep learning methods for GPR representation learning. Experiments include training a vanilla Autoencoder, Variational Autoencoder, and a Transformer-based Autoencoder on GPR A-scans. Additionally, we extend our analysis by fine-tuning a pre-trained Masked Autoencoder on image based GPR B-Scans to investigate the differences between the conventional pixel-based approach and our proposed A-scan-based approach. Through a meticulous analysis of the learned latent spaces across these methods, we assess the viability of self-supervised deep learning in encoding meaningful GPR representations for downstream tasks. This research contributes to the exploration of GPR's applicability in building rooftop diagnostics and underscores the potential of self-supervised deep learning for efficient representation learning in the absence of annotated data.
