Classification of leakage detections acquired by airborne thermography of district heating networks

We address the problem of reducing the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps. First, we use a building segmentation scheme in order to remove detections on buildings. Second, we extract features from the detections and use a Random forest classifier on the remaining detections. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system.