An Intelligent Method for Ice Thickness Identification Using Drone-Borne Ground-Penetrating Radar

Unmanned Aerial Vehicle-borne Ground-Penetrating Radar (UAV-GPR) has been used for ice thickness monitoring in lakes and rivers due to its non-contact measurement, high resolution, and operational flexibility. Existing algorithms can extract ice layer boundaries by tracking continuous bottom reflections in GPR images. However, they fail when the radar signal lacks a clear bottom reflection—a common condition in ice layers containing unfrozen water—and manual interpretation remains time-consuming. To address this limitation, this paper builds a freshwater ice GPR dataset covering both fully frozen and unfrozen water-bearing zones, and proposes a method for ice thickness identification based on the DeepLabv3+ neural network. The model performs pixel-level binary classification, labeling each pixel as ice layer or background, and generates a segmentation mask that constrains the subsequent thickness calculation to valid ice regions only. Field validation against drilling measurements demonstrates that the model achieves Intersection over Union (IoU) of 97.12% and an F1-score of 98.54% for ice layer identification, with a relative error in ice thickness measurement below 3% based on five borehole measurements. Field tests in two reservoirs across Tibet and Jilin, China, demonstrate that the proposed method can accurately characterize the distribution and thickness of the ice layer while effectively eliminating the interference of unfrozen water zones. The results demonstrate that the proposed method can provide automated, accurate ice thickness estimates for UAV-GPR surveys of freshwater ice.