A canopy graph pseudo-labelling method with LLM-derived species cohabitation priors achieves 5.6% higher accuracy than the best baseline on a real forest dataset, with expert-validated priors accurate to within 15%.
Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data.InformationFusion, 93:118–131
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From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
A canopy graph pseudo-labelling method with LLM-derived species cohabitation priors achieves 5.6% higher accuracy than the best baseline on a real forest dataset, with expert-validated priors accurate to within 15%.