---
language:
- en
- hi
- te
- ur
- id
license: mit
task_categories:
- question-answering
- multiple-choice
tags:
- apart-global-south-hack
- vlm-conflict
- cross-modal
- remote-sensing
- counterfactual
size_categories:
- n<1K
---

# 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

| Language          | Code | Rows |
|:------------------|:-----|-----:|
| English           | en   |   50 |
| Hindi             | hi   |   50 |
| Urdu              | ur   |   50 |
| Telugu            | te   |   50 |
| Bahasa Indonesia  | id   |   50 |

## Columns

| Column | Type | Description |
|:-------|:-----|:------------|
| `original_row_index` | int | Index linking back to the source RS-VQA row |
| `image` | image | Satellite / remote sensing image |
| `Original_Caption` | str | Caption describing the original image category |
| `Counterfactual_caption` | str | Caption with a minimal counterfactual edit |
| `Question` | str | Multiple-choice question probing the difference |
| `Image_bias_answer` | str | Correct answer based on image evidence |
| `Text_bias_answer` | str | Correct answer based on text (counterfactual) evidence |
| `Plausible_Distractor` | str | Plausible but incorrect distractor answer |
| `language` | str | Language of the row |

## 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

```python
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**](https://huggingface.co/datasets/AdaptLLM/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](https://arxiv.org/abs/2411.19930)

- **Original dataset:** [AdaptLLM/remote-sensing-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/remote-sensing-VQA-benchmark)
- **Project page:** [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)

```bibtex
@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}
}
```