---
license: apache-2.0
task_categories:
- multiple-choice
- visual-question-answering
language:
- en
size_categories:
- n<1K
configs:
- config_name: benchmark
  data_files:
  - split: test
    path: dataset.json
paperswithcode_id: mapeval-visual
tags:
- geospatial
---

# MapEval-Visual

This dataset was introduced in [MapEval](https://arxiv.org/abs/2501.00316)

## Example

![Image](example.jpg)

### Query
I am presently visiting Mount Royal Park . Could you please inform me about the nearby historical landmark?

### Options
1. Circle Stone
2. Secret pool
3. Maison William Caldwell Cottingham
4. Poste de cavalerie du Service de police de la Ville de Montreal

### Correct Option
1. Circle Stone

## Prerequisite

Download the [Vdata.zip](https://huggingface.co/datasets/MapEval/MapEval-Visual/resolve/main/Vdata.zip?download=true) and extract in the working directory. This directory contains all the images.

## Usage
```python
from datasets import load_dataset
import PIL.Image
# Load dataset
ds = load_dataset("MapEval/MapEval-Visual", name="benchmark")

for item in ds["test"]:
   
    # Start with a clear task description
    prompt = (
        "You are a highly intelligent assistant. "
        "Based on the given image, answer the multiple-choice question by selecting the correct option.\n\n"
        "Question:\n" + item["question"] + "\n\n"
        "Options:\n"
    )
    
    # List the options more clearly
    for i, option in enumerate(item["options"], start=1):
        prompt += f"{i}. {option}\n"
    
    # Add a concluding sentence to encourage selection of the answer
    prompt += "\nSelect the best option by choosing its number."
    
    # Load image from Vdata/ directory
    img = PIL.Image.open(item["context"])
    
    # Use the prompt as needed
    print([prompt, img])  # Replace with your processing logic
```

## Citation

If you use this dataset, please cite the original paper:

```
@article{dihan2024mapeval,
  title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
  author={Dihan, Mahir Labib and Hassan, Md Tanvir and Parvez, Md Tanvir and Hasan, Md Hasebul and Alam, Md Almash and Cheema, Muhammad Aamir and Ali, Mohammed Eunus and Parvez, Md Rizwan},
  journal={arXiv preprint arXiv:2501.00316},
  year={2024}
}
```