Assessing regression-based deep learning for river ice estimation from drone images

The concentration of frazil ice, crucial to the development of river ice covers and the numerical modeling of ice cover development, is challenging to measure in situ. Remote sensing using deep neural networks on images of frazil drift ice taken from drone is promising but faces challenges due to limited annotated datasets and difficulty in visually distinguishing ice types and boundaries. In this work, a method for acquiring and processing optical drone river ice images was developed to estimate the concentration of frazil drift ice, mostly in frazil slush form. Drone images were acquired on four mesoscale rivers (widths of ≈ 30 to 100 m) situated in the south of the Province of Quebec, Canada during the 2022–2023 and the 2023–2024 winter. A first Convolutional Neural Network was trained to perform an initial classification. This Convolutional Neural Network, the static ice model, was trained to segment the images in four classes: water, static ice, trees above water and other. Despite a few minor classification errors, the model was used to estimate the extent of static ice cover. Once the initial classification was made, the frazil drift ice concentration was estimated by taking into account only the flow zone. To do so, two Convolutional Neural Networks were trained with the same dataset but annotated with two different techniques: semantic segmentation and regression. Following the analysis of the results, it was concluded that regression is highly promising for estimating frazil drift ice concentration, particularly when the ice is in slush form and at high concentrations. The differences between the concentrations obtained using this method and those obtained manually are quite small (between 0 % and 2.2 %). With the same annotation effort as regression, the segmentation technique shows higher deviations (between 0.1 % and 9.4 %). The segmentation trained model encounters challenges in accurately identifying water areas surrounded by frazil and tend to extend frazil boundaries beyond their actual limits, which lead to an overestimation of the frazil drift ice concentration. These results confirm the potential of using drone imagery to train a regression-annotated Convolutional Neural Network for estimating frazil surface concentration in mesoscale rivers. • Frazil drift ice concentration is an important parameter for river ice modeling. • Using drone images and deep learning to estimate frazil drift ice concentration is promising. • Creation of a frazil slush image dataset in mesoscale river prone to ice jam in Southern Quebec. • Regression is highly promising for estimating frazil drift ice concentration.