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The AutoIce challenge, organized by multiple national and international agencies, seeks to advance the development of near-real-time sea ice products with improved spatial resolution, broader spatial and temporal coverage, and enhanced consistency. In this paper, we present a detailed description of our solutions and experimental results for the challenge. We have implemented an automated sea ice mapping pipeline based on a multi-task U-Net architecture, capable of predicting sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE) using Sentinel-1 SAR data. For model training and evaluation, we utilize the AI4Arctic dataset, which includes SAR imagery, corresponding passive microwave and auxiliary data, and ice chart-derived label maps. Among the submissions from over 30 teams worldwide, our team achieved the highest combined score of 86.3 %, as well as the highest scores on SIC (92.0 %) and SOD (88.6 %). Additionally, our result analysis showcases the effectiveness of various techniques, such as input SAR variable downscaling, spatial-temporal encoding, input feature selection, and loss function selection, in significantly improving the accuracy, efficiency, and robustness of deep learning-based sea ice mapping. [ABSTRACT FROM AUTHOR]