Elevation Models, Shadows, and Infrared: Integrating Datasets for Thermographic Leak Detection

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%.