ZS-TreeSeg: A Zero-Shot Framework for Tree Crown Instance Segmentation

2026-01-31

AI Summary

This work proposes a zero-shot segmentation framework to address the challenges of segmenting densely overlapping tree crowns in remote sensing imagery, where supervised methods suffer from high annotation costs and poor generalization. The approach innovatively adapts the topological flow field concept from cellular instance segmentation to crown delineation, integrating semantic priors with star-convex geometric constraints to mathematically separate touching crowns without any training. Built upon a Cellpose-SAM architecture, the method enforces instance decoupling through a vector convergence mechanism within the topological flow field. Experiments demonstrate that the framework achieves strong cross-sensor and cross-density generalization on the NEON and BAMFOREST datasets, efficiently producing high-quality tree crown instance labels.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 ZS-TreeSeg, a Zero-Shot framework that adapts from two mature tasks: 1) Canopy Semantic segmentation; and 2) Cells instance segmentation. By modeling tree crowns as star-convex objects within a topological flow field using Cellpose-SAM, the ZS-TreeSeg framework forces the mathematical 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.

Problem

• tree crown instance segmentation
• dense overlapping canopies
• zero-shot learning
• remote sensing
• under-segmentation

Innovation

• Zero-Shot
• Tree Crown Segmentation
• Instance Separation
• Topological Flow Field
• Foundation Model Adaptation