FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations

TL;DR

FG-TreeSeg introduces a training-free, flow-guided method for accurate tree crown segmentation in remote sensing, overcoming dense canopy challenges without requiring annotated data.

Contribution

It transfers biomedical flow-based delineation techniques to remote sensing, enabling robust, training-free tree crown segmentation across diverse datasets.

Findings

Generalizes well across different sensors and canopy densities.

Provides a training-free solution for tree crown segmentation.

Demonstrates robustness through experiments on NEON and BAMFOREST datasets.

Abstract

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.