---
title: PEACE
canonical_url: "https://www.modelscope.cn/datasets/microsoft/PEACE"
md_url: "https://www.modelscope.cn/datasets/microsoft/PEACE.md"
repository: microsoft/PEACE
last_updated: 2025-07-21
license: mit
storage_size: "682 MB"
downloads: 931
stars: 1
---

# PEACE

> PEACE - microsoft 在 ModelScope 开源的数据集。PEACE: Empowering Geologic Map Holistic Understanding with MLLMs

microsoft/PEACE 是 ModelScope 魔搭社区上的数据集，存储大小 682 MB，采用 mit 许可。

- **Repository**: microsoft/PEACE
- **License**: mit
- **Storage size**: 682 MB
- **Downloads**: 931
- **Stars**: 1
- **Last updated**: 2025-07-21

Source: https://www.modelscope.cn/datasets/microsoft/PEACE

---

# PEACE: Empowering Geologic Map Holistic Understanding with MLLMs

[[`Code`](https://github.com/microsoft/PEACE)] [[`Paper`](https://arxiv.org/pdf/2501.06184)] [[`Data`](https://huggingface.co/datasets/microsoft/PEACE)]

<p align="left">
    <img src="./images/GeoMap-Bench_Pipeline.png" width="800">
</p>

## Introduction
We construct a geologic map benchmark, GeoMap-Bench, to evaluate the performance of MLLMs on geologic map understanding across different abilities, the overview of it is as shown in below Table.

<table>
  <thead>
    <tr>
      <th style="text-align:left;" >Property</th>
      <th style="text-align:left;" >Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th style="text-align:left;"  rowspan="2"><strong>Source</strong></th>
      <th style="text-align:left;" ><strong>USGS(English)</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>CGS(Chinese)</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>Content</strong></th>
      <th style="text-align:left;" ><strong>Image-question pair with annotated answer</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>Scale</strong></th>
      <th style="text-align:left;" ><strong>124 images and 3,864 questions</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>Resolution</strong></th>
      <th style="text-align:left;" ><strong>6,146<sup>2</sup> pixels on average</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;"  rowspan="3"><strong>Question Type</strong></th>
      <th style="text-align:left;" ><strong>1.Multiple-choicequestion</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>2.Fill-in-the-blankquestion</strong></th>
    </tr>
    <tr>
      <th style="text-align:left;" ><strong>3.Essayquestion</strong></th>
    </tr>
    <tr>
      <td style="text-align:left;"  rowspan="5"><strong>Covering Ability</strong></td>
      <td style="text-align:left;" ><strong>1.Extracting</strong></td>
    </tr>
    <tr>
      <td style="text-align:left;" ><strong>2.Grounding</strong></td>
    </tr>
    <tr>
      <td style="text-align:left;" ><strong>3.Referring</strong></td>
    </tr>
    <tr>
      <td style="text-align:left;" ><strong>4.Reasoning</strong></td>
    </tr>
    <tr>
      <td style="text-align:left;" ><strong>5.Analyzing</strong></td>
    </tr>
    <tr>
      <td style="text-align:left;" ><strong>Defined Task</strong></td>
      <td style="text-align:left;" ><strong>25 tasks</strong></td>
    </tr>
  </tbody>
</table>

## Data Instance
```Python
{
    "question: "According to this geologic map, regarding the rock type in main map, which one has the smallest area among 4 choices?",
    "answer": "D",
    "type": "reasoning-area_comparison",
    "A": "Torok Formation",
    "B": "Surficial deposits, undivided (Holocene and Pleistocene)",
    "C": "Lower part",
    "D": "Alluvial deposits, undivided (Holocene and Pleistocene)",
    "mcq": true,
    "img_path": "16809_83756_4.jpg"
}
```

## Data Fields

- `question`: The question
- `answer`: The annotated answer
- `type`: The question type
- `A`: Choice A
- `B`: Choice B
- `C`: Choice C
- `D`: Choice D
- `mcq`: Whether the question is multiple-choice question
- `img_path`: The image path of geologic map

## Data Distribution
The distribution of evaluation abilities and tasks is demonstrated below.

<p align="left">
    <img src="./images/GeoMap-Bench_Distribution.png" width="600">
</p>

## Leaderboard
Through comprehensive experiments, GeoMap-Agent achieves an overall score of 0.811 on GeoMap-Bench, significantly outperforming 0.369 of GPT-4o.

| Method               | Extracting | Grounding | Referring | Reasoning | Analyzing | Overall  |
|----------------------|------------|-----------|-----------|-----------|-----------|----------|
| Random               | 0          | 0         | 0.250     | 0.250     | 0         | 0.100    |
| GPT-4o               | 0.219      | 0.128     | 0.378     | 0.507     | 0.612     | 0.369    |
| GeoMap-Agent         | **0.832**  | **0.920** | **0.886** | **0.588** | **0.831** | **0.811** |

## Citation
```
TBD
```

## License
The dataset is licensed under the MIT License.