Empowering Geologic Map Holistic Understanding with MLLMs

[📜 Paper] • [🤗 HF Dataset] • [🐱 GitHub Code] [🎞️ YouTube Video]

📢 News and Updates

• 2025/01/10: 🔥GitHub Code repo (GeoMap-Agent) released.
• 2025/01/10: 🔥HuggingFace Dataset repo (GeoMap-Bench) released.
• 2025/01/13: 🔥Arxiv paper (PEACE) released.
• 2025/02/27: 🔥Accepted by CVPR'25.
• 2025/03/25: 🔥Add metrics measurement script.
• 2025/04/22: 🔥Add interactive demo video.

📖 Table of Contents

• Introduction
• GeoMap-Bench
• GeoMap-Agent
• Leaderboard
• Data Display
• Quickstart
• Disclaimer
• Citation

🌟 Introduction

Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispensable in various fields, including disaster detection, resource exploration, and civil engineering.

📖 GeoMap-Bench

We present GeoMap-Bench, a new benchmark consisting of 124 geologic maps and 3,864 multimodal multiple-choice questions with diverse annotations. The distribution of questions in the GeoMap-Bench. It consists of 25 task types that measure critical geological map interpretation abilities across five aspects: grounding, extracting, referring, reasoning, and analyzing.

🌍 GeoMap-Agent

We introduce GeoMap-Agent, the inaugural agent designed for geologic map understanding, which features three modules: Hierarchical Information Extraction(HIE), Domain Knowledge Injection (DKI), and Prompt-enhanced Question Answering (PEQA). Inspired by the interdisciplinary collaboration among human scientists, an AI expert group acts as consultants, utilizing a diverse tool pool to comprehensively analyze questions.

🧮 Leaderboard

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

🔍 Data Display

We visualize the components of a typical geologic map, highlighting the complex nature of cartographic generalization. Additionally, we showcase sample questions from GeoMap-Bench and demonstrate how our GeoMap-Agent integrates various sources of contextual information to address them.

⏩ Quick Start

git clone https://github.com/microsoft/PEACE.git
cd PEACE

git lfs install
git lfs clone https://huggingface.co/datasets/microsoft/PEACE data

pip install gdown
gdown https://drive.google.com/uc?id=1f7dUdfA_W8He9czG6SoYQBmUsSPrA6MZ
unzip models.zip -d dependencies

pip install -r requirements.txt

python eval.py --copilot_mode HIE,DKI,PEQA --dataset_source usgs
python calc_metrics.py --copilot_mode HIE,DKI,PEQA --dataset_source usgs

⚡ Disclaimer

Due to the inherent limitations of large language models, issues such as hallucinations may occur.

🔗 Citation

@article{huang2025peace,
  title={PEACE: Empowering Geologic Map Holistic Understanding with MLLMs},
  author={Huang, Yangyu and Gao, Tianyi and Xu, Haoran and Zhao, Qihao and Song, Yang and Gui, Zhipeng and Lv, Tengchao and Chen, Hao and Cui, Lei and Li, Scarlett and others},
  journal={arXiv preprint arXiv:2501.06184},
  year={2025}
}

👀 License

This repository is licensed under the MIT License. The use of Ultralytics library is subject to the AGPL-3.0 License.