SelvaMask: Segmenting Trees in Tropical Forests and Beyond

2026-02-02

AI Summary

This study addresses the challenge of low segmentation accuracy for individual tree crowns in structurally dense tropical forests. To this end, the authors introduce SelvaMask, a high-quality benchmark dataset comprising over 8,800 manually annotated tree crowns across multiple tropical countries, and propose a modular detection–segmentation pipeline that integrates a vision foundation model (VFM) with a domain-specific detection prompter. By leveraging a detection prompting mechanism to effectively adapt the VFM to the target domain, the method substantially improves segmentation performance in dense forest regions. It achieves state-of-the-art results on SelvaMask, outperforming both zero-shot general-purpose models and fully supervised end-to-end approaches, while also demonstrating strong generalization across multiple external datasets from both tropical and temperate forests.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Tropical forests harbor most of the planet's tree biodiversity and are critical to global ecological balance. Canopy trees in particular play a disproportionate role in carbon storage and functioning of these ecosystems. Studying canopy trees at scale requires accurate delineation of individual tree crowns, typically performed using high-resolution aerial imagery. Despite advances in transformer-based models for individual tree crown segmentation, performance remains low in most forests, especially tropical ones. To this end, we introduce SelvaMask, a new tropical dataset containing over 8,800 manually delineated tree crowns across three Neotropical forest sites in Panama, Brazil, and Ecuador. SelvaMask features comprehensive annotations, including an inter-annotator agreement evaluation, capturing the dense structure of tropical forests and highlighting the difficulty of the task. Leveraging this benchmark, we propose a modular detection-segmentation pipeline that adapts vision foundation models (VFMs), using domain-specific detection-prompter. Our approach reaches state-of-the-art performance, outperforming both zero-shot generalist models and fully supervised end-to-end methods in dense tropical forests. We validate these gains on external tropical and temperate datasets, demonstrating that SelvaMask serves as both a challenging benchmark and a key enabler for generalized forest monitoring. Our code and dataset will be released publicly.

Problem

• tree crown segmentation
• tropical forests
• aerial imagery
• canopy trees
• biodiversity monitoring

Innovation

• vision foundation models
• tree crown segmentation
• tropical forest
• detection-prompter
• benchmark dataset