用多模态大模型提升遥感找矿能力，解决跨图推理难题。
MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal Large Language Models
- 分层判断与决策模块增强多图推理和光谱空间融合
- 在地质与高光谱数据上验证性能显著优于基线模型
- 专为找矿任务设计评测基准，适合遥感与地质研究者
遥感找矿对识别具有经济价值的矿产资源至关重要，但对多模态大语言模型（MLLMs）构成重大挑战，包括领域知识不足及跨多幅遥感图像推理困难，加剧了长上下文处理问题。为此，我们提出MineAgent，一种模块化框架，通过分层判断与决策模块提升多图像推理能力和空间-光谱融合效果。同时，我们构建了MineBench，一个专门用于评估MLLMs在特定找矿任务中表现的基准，基于地质与高光谱数据。大量实验表明MineAgent有效，展现出推动MLLM在遥感找矿中应用的巨大潜力。
原文摘要 · Abstract (English)
Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, further exacerbating long-context issues. To address these, we present MineAgent, a modular framework leveraging hierarchical judging and decision-making modules to improve multi-image reasoning and spatial-spectral integration. Complementing this, we propose MineBench, a benchmark specific for evaluating MLLMs in domain-specific mineral exploration tasks using geological and hyperspectral data. Extensive experiments demonstrate the effectiveness of MineAgent, highlighting its potential to advance MLLMs in remote-sensing mineral exploration.
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