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%.
Advances in hyperspectral image classification methods with small samples: A review.RemoteSensing, 15(15):3795
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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%.