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

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2303.07403.pdf (1.13 MB)

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https://arxiv.org/abs/2303.07403

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https://doi.org/10.48550/arXiv.2303.07403
 http://hdl.handle.net/11603/27595

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UMBC Information Systems Department
UMBC Student Collection

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https://orcid.org/0000-0002-8545-0830

Date

2023-03-13

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journal articles
preprints
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Attribution 4.0 International (CC BY 4.0)

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Abstract

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