Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery

2026-05-15

AI Summary

This study addresses the challenges of instance segmentation of broadleaf tree crowns, which suffer from high morphological variability and ambiguous crown boundaries, leading to limited accuracy and poor generalization. Leveraging a dataset of 18,507 high-quality manually annotated crown polygons, we develop a deep learning framework centered on Mask2Former with various backbone networks, trained and evaluated exclusively on UAV-derived RGB orthoimagery. Our approach achieves high-detail, generalizable individual tree crown segmentation across diverse ecological settings—including temperate forests in Japan and tropical rainforests in Borneo—demonstrating for the first time the critical role of large-scale, fine-grained annotations in enhancing model generalization. The trained model has been integrated into DF Scanner Pro software to support operational forest monitoring applications.

Abstract

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 architectures. The best model achieved high segmentation performance in structurally complex broadleaf forests using only RGB imagery. This performance was maintained when applied to geographically distinct forests within Japan, as well as to biologically distinct tropical rainforests in Borneo. These results demonstrate that using a large number of high-quality annotated datasets is critical for achieving detailed and generalizable crown segmentation across diverse forest ecosystems. The developed model has been integrated into DF Scanner Pro, a software that supports practical forest monitoring using UAVs, and this implementation is expected to enable a wide range of users to analyze tree-level information in broadleaf forest from UAVs.

Problem

• broadleaf forest
• tree crown segmentation
• UAV imagery
• instance segmentation
• forest monitoring

Innovation

• instance segmentation
• broadleaf forest
• UAV imagery
• Mask2Former
• generalizable model