apart-global-south-hack/remote_sensing_VQA_multilingual

Remote Sensing VQA — Multilingual

A multilingual counterfactual MCQ dataset built from remote sensing / satellite imagery.

Each row contains a satellite image, two captions (original vs counterfactual), and a multiple-choice question probing whether a VLM follows the image or the misleading text.

Languages

Columns

Dataset Statistics

• —Total rows: 250 (50 per language × 5 languages)
• —Source: Remote sensing VQA dataset with programmatic counterfactual captions
• —Conflict types: Category/land-use misattribution on satellite imagery

Usage

from datasets import load_dataset

ds = load_dataset("apart-global-south-hack/remote_sensing_VQA_multilingual", split="train")
print(ds[0])

Original Source

This dataset is built from the **Remote Sensing VQA Benchmark** by AdaptLLM (Cheng et al., 2024), which provides remote sensing visual instruction tasks for evaluating MLLMs.

Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Wayne Xin Zhao, Zhongzhi Luan, Bo Dai, and Zhenliang Zhang. 2024. On Domain-Specific Post-Training for Multimodal Large Language Models. arXiv:2411.19930. Published at EMNLP 2025. Paper: arxiv.org/abs/2411.19930

• —Original dataset: AdaptLLM/remote-sensing-VQA-benchmark
• —Project page: Adapt-MLLM-to-Domains

@article{cheng2024adamllm,
  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
  journal={arXiv preprint arXiv:2411.19930},
  year={2024}
}