| --- |
| 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., 2025), which provides remote sensing visual instruction tasks for evaluating MLLMs. |
|
|
| > Ran Cheng et al. 2025. |
| > *On Domain-Specific Post-Training for Multimodal Large Language Models.* |
| > 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 |
| @inproceedings{cheng2025domain, |
| title={On Domain-Specific Post-Training for Multimodal Large Language Models}, |
| author={Cheng, Ran and others}, |
| booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
| year={2025} |
| } |
| ``` |
|
|
