| Abstract |
Thermal bridges represent critical weaknesses in building envelope materials, increasing energy consumption by 40 % and reducing occupant comfort. This study proposes a novel non-destructive methodology for monitoring material performance in urban buildings using aerial multi-modality imaging. Two models were developed: 1) a customized deep learning (DL) model based on YOLOv9, trained on a multimodal dataset of 6927 visible, thermal, and LiDAR data, and 2) a physics-based heat loss model for quantifying thermal bridges. The DL model achieved a precision of 0.72, detecting an average of 3.33 thermal bridges per image, with an inference time under 30.3 milliseconds on an NVIDIA A100 GPU, enabling real-time city-scale diagnostics. Post-processing using an ANN refined bounding box predictions and increased precision to 0.763 and reduced localization error by 14.7 %. The heat loss model estimated surface losses ranging from 4.02 to 37.85 W/m², with an average of 22.37 W/m². A sensitivity analysis revealed that detection errors caused up to 28.55 % relative error in heat loss estimates, with missed detections having the largest impact on performance. Validation on the AGAP (RGB and thermal fused) dataset confirmed generalizability, achieving 80 % precision. Comparative evaluation showed YOLOv9-E outperformed other state-of-the-art models such as MaskRCNN, and YOLOv7. Projections suggest that undetected bridges on a typical 100 m² façade may lead to up to 11,409 kWh of excess heating demand over six months. This integrated solution offers a scalable, automated framework for non-destructive testing (NDT), material-level diagnostics, and energy-efficient retrofitting in smart city applications. © 2025 |
