README.md

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

# MapEval-Visual

This dataset was introduced in [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://arxiv.org/abs/2501.00316)

[Project page](https://mapeval.github.io/)

# 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.

"
        "Question:
" + item["question"] + "

"
        "Options:
"
    )
    
    # List the options more clearly
    for i, option in enumerate(item["options"], start=1):
        prompt += f"{i}. {option}
"
    
    # Add a concluding sentence to encourage selection of the answer
    prompt += "
Select 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

    # Then match the output with item["answer"] or item["options"][item["answer"]-1]
    # If item["answer"] == 0: then it's unanswerable
```

# Leaderboard

| Model                     | Overall | Place Info | Nearby | Routing | Counting | Unanswerable |
|---------------------------|:-------:|:----------:|:------:|:-------:|:--------:|:------------:|
| Claude-3.5-Sonnet         | **61.65**   | **82.64**      | 55.56  | **45.00**   | **47.73**    | **90.00**        |
| GPT-4o                    | 58.90   | 76.86      | **57.78**  | 50.00   | **47.73**    | 40.00        |
| Gemini-1.5-Pro            | 56.14   | 76.86      | 56.67  | 43.75   | 32.95    | 80.00        |
| GPT-4-Turbo               | 55.89   | 75.21      | 56.67  | 42.50   | 44.32    | 40.00        |
| Gemini-1.5-Flash          | 51.94   | 70.25      | 56.47  | 38.36   | 32.95    | 55.00        |
| GPT-4o-mini               | 50.13   | 77.69      | 47.78  | 41.25   | 28.41    | 25.00        |
| Qwen2-VL-7B-Instruct      | 51.63   | 71.07      | 48.89  | 40.00   | 40.91    | 40.00        |
| Glm-4v-9b                 | 48.12   | 73.55      | 42.22  | 41.25   | 34.09    | 10.00        |
| InternLm-Xcomposer2       | 43.11   | 70.41      | 48.89  | 43.75   | 34.09    | 10.00        |
| MiniCPM-Llama3-V-2.5      | 40.60   | 60.33      | 32.22  | 32.50   | 31.82    | 30.00        |
| Llama-3-VILA1.5-8B        | 32.99   | 46.90      | 32.22  | 28.75   | 26.14    | 5.00         |
| DocOwl1.5                 | 31.08   | 43.80      | 23.33  | 32.50   | 27.27    | 0.00         |
| Llava-v1.6-Mistral-7B-hf  | 31.33   | 42.15      | 28.89  | 32.50   | 21.59    | 15.00        |
| Paligemma-3B-mix-224      | 30.58   | 37.19      | 25.56  | 38.75   | 23.86    | 10.00        |
| Llava-1.5-7B-hf           | 20.05   | 22.31      | 18.89  | 13.75   | 28.41    | 0.00         |
| Human                     | 82.23   | 81.67      | 82.42  | 85.18   | 78.41    | 65.00        |

# 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}
}
```