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

arXiv: https://arxiv.org/abs/2602.00470 · HTML: https://mlsanity.com/paper/2602.00470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [OAM-TCD: A globally diverse dataset of high-resolution tree cover maps](https://mlsanity.com/paper/2407.11743/) (2024)
- [Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery](https://mlsanity.com/paper/2506.04970/) (2025)
- [Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery](https://mlsanity.com/paper/2503.20199/) (2025)
- [Multi-modal Uncertainty Robust Tree Cover Segmentation For High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2509.04870/) (2025)

## Built on by (most cited first, 1 of 1)

- [Latent Commonality Expectation-Maximisation for Box-supervised Tree Crown Instance Segmentation](https://mlsanity.com/paper/2609.26549/) (2026)

## Abstract (arXiv)

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.