3D Models for Thermal Anomaly Detection in Building Envelopes

Thermal evaluation of buildings is essential to improving energy efficiency and mitigating the environmental impact of the construction sector. However, conventional methodologies face significant limitations, as they cannot integrate thermal and RGB data into a single 3D model, hindering the accurate identification of anomalies such as thermal bridges, heat losses, or areas with poor insulation. These challenges are further complicated by difficulties in aligning thermal and RGB images due to differences in optical properties and sensor resolutions, as well as the complexity of processing large datasets. This work proposes an innovative methodology to generate integrated 3D models combining thermal and RGB data, based on UAS-captured imagery using thermal cameras and RGB sensors, enabling the creation of cohesive 3D models that incorporate both the building’s geometry and its thermal characteristics. Using these models, statistical analysis of temperature distribution is conducted to identify thermal anomalies. Significant deviations are categorized into two main types: linear anomalies (e.g., thermal bridges) and surface anomalies (e.g., areas of heat loss), using techniques like Principal Component Analysis (PCA). This approach enables the semi-automatic detection of critical areas, optimizing the evaluation of thermal performance. The proposed methodology has been validated in a real-world case study, successfully detecting thermal anomalies with high precision and classifying their energy impact. The results were incorporated into digital twins that represent not only the geometry of the building but also its thermal behaviour, providing a powerful tool for energy audits and rehabilitation strategies. This approach represents a significant step forward in thermal inspection, promoting innovative solutions to optimize building energy performance and fostering more sustainable and resilient urban environments.