Monitoring district heating networks, which distribute hot water or steam across urban areas, is a critical maintenance task. Aerial thermal infrared imagery offers an efficient tool for this purpose. Conventional detection of leakages in thermal raster data relies on data processing algorithms, but often suffers from false positives and missed detections. In this paper, we present a methodology for reliably detecting temperature anomalies—whether caused by actual leakages or by weak materials that may lead to future leakages— while minimizing false detections. Our approach generates auxiliary features to visually guide operators toward true detections. Specifically, we integrate thermal raster data with thermal isolines and statistical analyses that compare pipeline-axis temperatures to their surroundings. Cold pipeline segments are automatically omitted, while hot segments are highlighted according to their local temperature differences, thereby increasing the clarity and reliability of findings. To resolve ambiguities introduced by thermal hotspots near buildings or parking areas, we further incorporate Google Street View data for contextual verification. We evaluated the methodology on an airborne long-wavelength thermal dataset acquired over Nuremberg, Germany. Compared with conventional raster-based screening, our approach yielded a substantial improvement: the number of detected anomalies increased from 908 to 1,660, while the number of false positives was significantly reduced. An additional advantage of the proposed method is its ability to detect subtle temperature differences, enabling the identification of small but critical energy losses that might otherwise remain unnoticed.