NormalView method achieves 95.5% accuracy in tree species classification using lidar data

Researchers have developed NormalView, a novel deep learning method for classifying tree species using lidar data. This projection-based approach embeds geometric information into 2D projections, which are then fed into the YOLOv11 image classification network. The method achieved high accuracy, with 95.5% on mobile laser scanning (MLS) data and 91.8% on airborne laser scanning (ALS) data. The study also found that incorporating multispectral radiometric intensity information from multiple scanner channels can further improve classification performance. AI

IMPACT This research demonstrates a novel deep learning approach for environmental monitoring, potentially improving forestry management and ecological studies.

RANK_REASON The cluster contains an academic paper detailing a new method and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

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• airborne laser scanning
• arXiv
• Computer vision and pattern recognition
• Juho Korkeala
• Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook
• NormalView
• YOLOv11

• paper
• other

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