SelvaMask: Segmenting Trees in Tropical Forests and Beyond

• URL: http://arxiv.org/abs/2602.02426v1
• Date: Mon, 02 Feb 2026 18:26:56 GMT
• Title: SelvaMask: Segmenting Trees in Tropical Forests and Beyond
• Authors: Simon-Olivier Duguay, Hugo Baudchon, Etienne Laliberté, Helene Muller-Landau, Gonzalo Rivas-Torres, Arthur Ouaknine,
• Abstract summary: SelvaMask is a new dataset containing over 8,800 manually delineated tree crowns across three Neotropical forest sites in Panama, Brazil, and Ecuador.<n>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.
• Score: 1.5215420208038806
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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