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For glaciologists, studying ice sheets from the polar regions is critical.With the advancement of deep learning techniques, we can now extract high-levelinformation from the ice sheet data (e.g., estimating the ice layer thickness,predicting the ice accumulation for upcoming years, etc.). However, avision-based conversational deep learning approach has not been explored yet,where scientists can get information by asking questions about images. In thispaper, we have introduced the task of Visual Question Answering (VQA) onremote-sensed ice sheet imagery. To study, we have presented a unique VQAdataset, Polar-VQA, in this study. All the images in this dataset werecollected using four types of airborne radars. The main objective of thisresearch is to highlight the importance of VQA in the context of ice sheetresearch and conduct a baseline study of existing VQA approaches on Polar-VQAdataset.

Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region