Thermal Bridge Detection Using Multi-Modality Imaging
Thermal bridges in building envelopes are localized weaknesses that significantly affect energy performance and occupant comfort. These areas of elevated heat transfer can increase energy consumption by up to 40 %, highlighting the need for precise identification and mitigation strategies. Recent advancements in remote sensing and machine learning offer promising avenues for non-destructive monitoring of material performance in urban buildings. By integrating aerial multi-modality imaging, which combines visible, thermal, and LiDAR data, researchers can achieve real-time, city-scale diagnostics, providing a foundation for energy-efficient retrofitting and sustainable urban development. Development of a Deep Learning Framework This study presents a customized deep learning (DL) approach based on YOLOv9, tailored for thermal bridge detection using a multimodal dataset of 6,927 images. By leveraging the complementary strengths of visible, thermal, and LiDAR data, the model effectively ...