MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal Large Language Models
Journal:
arXiv
Published Date:
Dec 23, 2024
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.
