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arXiv · 2412.17339

MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal Large Language Models

Beibei Yu·Tao Shen·Hongbin Na·Ling Chen·Denqi Li
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Abstract

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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Beibei Yu, Tao Shen, Hongbin Na, Ling Chen, Denqi Li. 2024-12-23. MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal Large Language Models. https://arxiv.org/abs/2412.17339

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