ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering

Zhen Yao; Likai Wang; Yuming Yang; Zhihao Zheng; Bo Lang; Qiuyu Tang; Jialu Sheng; Jingqi Xu; Yuehai Yang; Jumal Barker; Xiaowen Ying; Mooi Choo Chuah

arXiv · 2609.35507

ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering

Zhen Yao·Likai Wang·Yuming Yang·Zhihao Zheng·Bo Lang·Qiuyu Tang·Jialu Sheng·Jingqi Xu·Yuehai Yang·Jumal Barker·Xiaowen Ying·Mooi Choo Chuah

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Abstract

Multimodal Large Language Models (MLLMs) have demonstrated remarkable advances in remote sensing. However, existing remote sensing multimodal reasoning benchmarks exhibit two critical limitations: they rely on (i) template-driven questions, which causes repetitive questions; and (ii) static images that fail to capture the inherent temporal nature of drone/UAV videos. This leaves systematic evaluation of remote sensing video reasoning largely unexplored. To address this gap, we introduce ReVA, a new dataset for remote sensing video question answering, designed to assess spatiotemporal, scene-centric, and reasoning-oriented capabilities of MLLMs. ReVA comprises 2,438 drone videos spanning 18 cities worldwide (580K frames) and 22K high-quality question-answer pairs across 11 challenging QA tasks. We develop a semi-automatic annotation pipeline that leverages Text LLMs and MLLMs for question-answer generation with human verification. We evaluate 23 proprietary and open-source Video LLMs on ReVA, exposing fundamental limitations of current models. These findings position ReVA as a critical benchmark toward better remote sensing video understanding and temporal reasoning capabilities for real-world deployments. Our code and dataset are available at: https://github.com/zyaocoder/ReVA

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Zhen Yao, Likai Wang, Yuming Yang, Zhihao Zheng, Bo Lang, Qiuyu Tang, Jialu Sheng, Jingqi Xu, Yuehai Yang, Jumal Barker, Xiaowen Ying, Mooi Choo Chuah. 2026-09-28. ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering. https://arxiv.org/abs/2609.35507

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