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--- |
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license: apache-2.0 |
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task_categories: |
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- multiple-choice |
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- visual-question-answering |
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language: |
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- en |
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size_categories: |
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- n<1K |
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configs: |
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- config_name: benchmark |
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data_files: |
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- split: test |
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path: dataset.json |
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paperswithcode_id: mapeval-visual |
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tags: |
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- geospatial |
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--- |
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# MapEval-Visual |
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This dataset was introduced in [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://arxiv.org/abs/2501.00316) |
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## Example |
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 |
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### Query |
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I am presently visiting Mount Royal Park . Could you please inform me about the nearby historical landmark? |
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### Options |
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1. Circle Stone |
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2. Secret pool |
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3. Maison William Caldwell Cottingham |
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4. Poste de cavalerie du Service de police de la Ville de Montreal |
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### Correct Option |
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1. Circle Stone |
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## Prerequisite |
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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. |
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## Usage |
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```python |
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from datasets import load_dataset |
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import PIL.Image |
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# Load dataset |
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ds = load_dataset("MapEval/MapEval-Visual", name="benchmark") |
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for item in ds["test"]: |
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# Start with a clear task description |
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prompt = ( |
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"You are a highly intelligent assistant. " |
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"Based on the given image, answer the multiple-choice question by selecting the correct option.\n\n" |
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"Question:\n" + item["question"] + "\n\n" |
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"Options:\n" |
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) |
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# List the options more clearly |
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for i, option in enumerate(item["options"], start=1): |
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prompt += f"{i}. {option}\n" |
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# Add a concluding sentence to encourage selection of the answer |
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prompt += "\nSelect the best option by choosing its number." |
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# Load image from Vdata/ directory |
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img = PIL.Image.open(item["context"]) |
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# Use the prompt as needed |
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print([prompt, img]) # Replace with your processing logic |
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``` |
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## Citation |
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If you use this dataset, please cite the original paper: |
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``` |
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@article{dihan2024mapeval, |
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title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models}, |
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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}, |
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journal={arXiv preprint arXiv:2501.00316}, |
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year={2024} |
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} |
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``` |
