# AGL-UNet: Adaptive Global–Local Modulated U-Net for Multitask Sea Ice Mapping

## TL;DR

This paper introduces AGL-UNet, a new U-Net-based framework for sea ice mapping that improves multi-task performance using adaptive global and local feature modulation.

## Contribution

The novel AGL-UNet framework introduces ARC and GLCM blocks for multi-sensor fusion and adaptive feature modulation in sea ice mapping.

## Key findings

- AGL-UNet outperforms the best method in the AutoIce Challenge with a 1.33% improvement in combined score.
- F1 scores for SOD and FLOE increase by 2.85% and 3.44%, respectively.
- Ablation studies confirm the effectiveness of the proposed blocks and adaptive weighting strategy.

## Abstract

The increasing demand for Arctic route planning, climate change studies, and the growing volume of satellite sensor data have made automated sea ice mapping an essential task. In this study, we propose a multi-task sea ice mapping framework based on the U-Net architecture, which supports multi-sensor data integration and automatically modulates global and local features. The model consists of ARC blocks for enhanced multi-sensor feature fusion, a GLCM block for non-local and local feature modulation, and an adaptive loss weighting strategy to balance multi-task training. The proposed method is evaluated on the AI4Arctic RTT dataset, which includes multi-sensor inputs and ice chart-derived labels. Compared with the best-performing method in the AutoIce Challenge, the proposed approach achieves a 1.33% improvement in the combined score. In addition, the F1 scores for stage of development (SOD) and floe size (FLOE) increase by 2.85% and 3.44%, respectively. Although the R2 score for SIC shows a slight decrease of 1.25%, this behavior is consistent with the practical trade-offs commonly observed in multi-task optimization. Ablation studies further demonstrate the effectiveness of the proposed blocks and the multi-task adaptive weighting strategy, confirming their potential for handling multi-sensor data and supporting ocean environment monitoring.