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

Source: [Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region](<https://www.sota2.com/research/paper/68fa6ecb1e209131cea422ea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:06:49.536Z

Authors: Argho Sarkar, Maryam Rahnemoonfar

Published: 2023-03-13T18:29:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.07403>)

[PDF](<https://arxiv.org/pdf/2303.07403.pdf>)