Papers
1
Total Citations
43
H-Index
1
About
Teng Long is a researcher working at the intersection of artificial intelligence and geoscience, with a particular focus on applying machine learning and computer vision techniques to mineral identification and classification. His most recognized work, a comprehensive 2022 review on artificial intelligence technologies in mineral identification, has garnered 43 citations and stands as a valuable reference for researchers bridging the gap between deep learning methodologies and geological applications. In this influential paper, Long examines how AI-driven approaches — spanning robotics, language recognition, and image identification — can be leveraged to automate and enhance the accuracy of mineral classification and visualization, tasks traditionally reliant on expert human interpretation. By consolidating advancements across these domains into a unified framework, his work has helped accelerate the adoption of intelligent systems within the mineralogy and earth sciences communities. Long's contributions are particularly timely given the growing demand for efficient, scalable mineral analysis in resource exploration and environmental monitoring. His research serves as an essential guide for students and practitioners seeking to understand how modern AI architectures can transform conventional geological workflows.
Research Focus
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Top Papers
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