Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery
We present a highly detailed instance segmentation model for delineating individual tree crowns in natural broadleaf forests using aerial imagery acquired by unmanned aerial vehicles (UAVs). Tree crown delineation in broadleaf forests is more challenging than in other forest types due to diversity of crown shapes and the lack of clearly defined treetops. To address this issue, we developed a deep-learning-based crown segmentation model trained on high-quality annotated crown outlines. We manually delineated 18,507 crown polygons from orthomosaic images collected across seven forests in Japan by skilled annotators, and developed a model based on Mask2Former with multiple backbone architect...
| Authors | Mitsutaka Nakada, Takahiko Ikebata, Kengo Ikebata et al. |
|---|---|
| Primary category | eess.IV |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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