Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery
Detecting dead trees in aerial imagery is vital for assessing forest health, especially as tree mortality increases globally due to climate change, but domain variability and scarce labeled data often limit model generalization. This study advances the TreeMort-1T-UNet (Tree Mortality 1-Task U-Net) model, initially trained on Finnish aerial imagery (source domain), by applying knowledge distillation (KD) to adapt it to various target domains, including Polish, German, and Estonian datasets representing diverse forest types. We assess four KD variants: Basic, Self, Feature-level, and Ensemble, against a fine-tuning baseline, using Mean Tree IoU, Instance F1-score, Instance Precision, and M...
| Authors | Anis Ur Rahman, Mete Ahishali, Einari Heinaro et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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