Research on semantic segmentation algorithm of high latitude urban river ice based on deep transfer learning

Automated observation methods for monitoring river ice in high-latitude urban areas are crucial for resource utilization, risk assessment, and navigation. However, current research lacks actual-scale river ice classification, such as low-altitude surveys. This study established a dataset of river ice in the Songhua River near Harbin, Northeast China, using UAV aerial photography and applied the RININet semantic segmentation algorithm for precise classification of different ice types in low-altitude aerial remote sensing images. To address environmental challenges, a feature extraction method integrating channel and spatial attention mechanisms was adopted, along with an improved pyramid pool structure to enhance feature recognition. Additionally, a two-stage transfer learning method established an ice recognition database, overcoming issues like small data volume and high annotation costs. Comparative evaluation metrics demonstrated the high accuracy of the semantic segmentation framework. Furthermore, a method for estimating ice blockage risk was proposed, applicable to various urban river ice management tasks, with practical significance.