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The massive shift in temperatures in the Arctic region has caused the increased Albedo effect as higher amount of solar energy is absorbed in the darker surface due to melting ice and snow. This continuous regional warming results in further melting of glaciers and loss of sea ice. Arctic melt ponds are important indicators of Arctic climate change. High-resolution aerial photographs are invaluable for identifying different sea ice features and are great source for validating, tuning, and improving climate models. Due to the complex shapes and unpredictable boundaries of melt ponds, it is extremely tedious, taxing, and time-consuming to manually analyze these remote sensing data that lead to the need for automatizing the technique. Deep learning is a powerful tool for semantic segmentation, and one of the most popular deep learning architectures for feature cascading and effective pixel classification is the UNet architecture. We introduce an automatic and robust technique to predict the bounding boxes for melt ponds using a Multiclass Recurrent Residual UNet (R2UNet) with UNet as a base model. R2UNet mainly consists of two important components in the architecture namely residual connection and recurrent block in each layer. The residual learning approach prevents vanishing gradients in deep networks by introducing shortcut connections, and the recurrent block, which provides a feedback connection in a loop, allows outputs of a layer to be influenced by subsequent inputs to the same layer. The algorithm is evaluated on Healy-Oden Trans Arctic Expedition (HO-TRAX) dataset containing melt ponds obtained during helicopter photography flights between 5 August and 30 September 2005. The testing and evaluation results show that R2UNet provides improved and superior performance when compared to UNet, Residual UNet (Res-UNet) and Recurrent U-Net (R-UNet).
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