Datasets:

apart-global-south-hack
/

remote_sensing_VQA_multilingual

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

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., 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

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

@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}
}