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FOR-age: Benchmarking individual tree age estimation using 3D deep learning on dense laser scanning data
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DOI：10.1016/j.rse.2026.115462.png)
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Abstract
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• Tree age predicted from tree point clouds using deep learning methods. • Dataset includes 1700 trees from boreal forests in Northern Europe. • Transformer models outperform simpler alternatives for age prediction. • Pretrained segmentation models used as backbones for regression. • Models generalize across species and laser scanning platforms.
Keywords:
Old-growth
Biodiversity
Tree growth
Forestry
Artificial intelligence (AI)
ForestFormer3D
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