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%.
TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classi- fication in remote sensing.Earth SystemScience Data, 15:681–695
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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%.