Detection of Thermal Anomalies on Building Façades Using Infrared Thermography and Supervised Learning

We propose a cost-effective, non-intrusive approach to assess the quality of the thermal isolation of the building stock in a given municipality or small city. Our prototype measurement system, mounted on a vehicle, acquires geotagged optical and infrared images from the street-side of the buildings. A basic approach to detect gross thermal anomalies, such as thermal bridges, via a binary classifier trained on 2000 carefully labeled infrared images is demonstrated. Thermal anomalies are identified with a precision score of around 89.2 % and 75.6 % recall on a test dataset of 1184 images infrared recorded in Hergiswil (NW). The enabled automated assessment helps to identify retrofitting targets and supports increasing the renovation rate.