FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations
For remote sensing researchers needing tree crown segmentation without costly annotations, this work offers a training-free solution that generalizes across datasets, though it adapts an existing method from another domain.
FG-TreeSeg proposes a training-free framework for tree crown instance segmentation that adapts flow-based delineation from biomedical imaging to remote sensing, achieving robust generalization across diverse sensor types and canopy densities without requiring instance annotations.
Individual tree crown segmentation is an important task in remote sensing for forest biomass estimation and ecological monitoring. However, accurate delineation in dense, overlapping canopies remains a bottleneck. While supervised deep learning methods suffer from high annotation costs and limited generalization, emerging foundation models (e.g., Segment Anything Model) often lack domain knowledge, leading to under-segmentation in dense clusters. To bridge this gap, we propose FG-TreeSeg, a training-free framework for tree crown instance segmentation that transfers flow-based delineation from biomedical imaging to remote sensing. By modeling tree crowns as star-convex objects within a topological flow field using Cellpose-SAM, the FG-TreeSeg framework forces the separation of touching tree crown instances based on vector convergence. Experiments on the NEON and BAMFOREST datasets and visual inspection demonstrate that our framework generalizes robustly across diverse sensor types and canopy densities, which can offer a training-free solution for tree crown instance segmentation and labels generation.